This R script is used to validate the points in the ReSurvey database
using RS indicators (indices + phenology + canopy height).
Distributions all bioregions
Indices
# Define a function to create histograms
plot_histogram <- function(data, x_var, x_label) {
ggplot(data %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
aes(x = !!sym(x_var))) +
geom_histogram(color = "black", fill = "white") +
labs(x = x_label, y = "Frequency") +
theme_bw()
}
# Define a function to create plots with violin + boxplot + points
distr_plot <- function(data, y_vars, y_labels) {
for (i in seq_along(y_vars)) {
y_var <- y_vars[[i]]
y_label <- y_labels[[i]]
p <- ggplot(data = data %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
aes(x = EUNISa_1_descr, y = !!sym(y_var), fill = EUNISa_1_descr)) +
geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
geom_point(aes(y = !!sym(y_var), color = EUNISa_1_descr),
position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1,
label = length(x)),
geom = "text", aes(label = ..label..), vjust = 0.5) +
labs(y = y_label, x = "EUNIS level 1") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
guides(fill = FALSE, color = FALSE) +
theme_bw() + coord_flip()
print(p)
}
}
Ranges of min and max:
range(data_validation$NDVI_max, na.rm = T) # NDVI_max > 1 (slightly)
[1] 0.06446001 1.09902698
range(data_validation$NDMI_max, na.rm = T) # NDMI_max > 1 (slightly)
[1] -0.1994688 1.0924980
range(data_validation$NDWI_max, na.rm = T)
[1] -0.7283135 0.6050488
range(data_validation$SAVI_max, na.rm = T)
[1] 0.01849304 0.85933671
range(data_validation$EVI_max, na.rm = T) # EVI_max > 1 (slightly)
[1] 0.05318326 1.14645898
range(data_validation$NDVI_min, na.rm = T)
[1] -0.6891069 0.8422586
range(data_validation$NDMI_min, na.rm = T)
[1] -0.6620149 0.5395376
range(data_validation$NDWI_min, na.rm = T) # NDWI_min < -1 (slightly)
[1] -1.0579643 -0.0262825
range(data_validation$SAVI_min, na.rm = T)
[1] -0.4959691 0.6207103
range(data_validation$EVI_min, na.rm = T) # EVI_min < -1!
[1] -4.1368512 0.6563168
nrow(data_validation %>% dplyr::filter(NDVI_max > 1))
[1] 12
nrow(data_validation %>% dplyr::filter(NDMI_max > 1))
[1] 70
nrow(data_validation %>% dplyr::filter(EVI_max > 1))
[1] 5
nrow(data_validation %>% dplyr::filter(NDWI_min < -1))
[1] 11
nrow(data_validation %>% dplyr::filter(EVI_min < -1))
[1] 15
Histograms to check that max and min values are ok:
plot_histogram(data_validation, "NDVI_max", "NDVI max")

plot_histogram(data_validation, "NDMI_max", "NDMI max")

plot_histogram(data_validation, "NDWI_max", "NDWI max")

plot_histogram(data_validation, "SAVI_max", "SAVI max")

plot_histogram(data_validation, "EVI_max", "EVI max")

plot_histogram(data_validation, "NDVI_min", "NDVI min")

plot_histogram(data_validation, "NDMI_min", "NDMI min")

plot_histogram(data_validation, "NDWI_min", "NDWI min")

plot_histogram(data_validation, "SAVI_min", "SAVI min")

plot_histogram(data_validation %>%
dplyr::filter(EVI_min > -1.5), "EVI_min", "EVI min")

nrow(data_validation %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
dplyr::filter(EVI_max > 1 | EVI_max < -1))
[1] 5
data_validation %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q"))%>%
dplyr::filter(EVI_max > 1 | EVI_max < -1) %>%
count(biogeo, unit)
Most EVI values are ok!
Distribution plots:
distr_plot(data_validation %>% dplyr::filter(EVI_min > -0.5),
c("NDVI_max", "EVI_max", "SAVI_max", "NDMI_max", "NDWI_max",
"NDVI_min", "EVI_min", "SAVI_min", "NDMI_min", "NDWI_min"),
c("NDVI_max", "EVI_max", "SAVI_max", "NDMI_max", "NDWI_max",
"NDVI_min", "EVI_min", "SAVI_min", "NDMI_min", "NDWI_min"))










CH
distr_plot(data_validation, "canopy_height", "Canopy height (m)")

Show habitats with CH categories
ggplot(data_validation %>%
# Keep only forests, grasslands, shrublands and wetlands
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
mutate(CH_cat =
factor(
case_when(canopy_height == 0 ~ "0 m",
canopy_height > 0 & canopy_height <= 1 ~ "0-1 m",
canopy_height > 1 & canopy_height <=2 ~ "1-2 m",
canopy_height > 2 & canopy_height <=5 ~ "2-5 m",
canopy_height > 5 & canopy_height <=8 ~ "5-8 m",
canopy_height > 8 ~ "> 8 m",
is.na(canopy_height) ~ NA_character_),
levels = c(
"0 m", "0-1 m", "1-2 m", "2-5 m", "5-8 m", "> 8 m"))),
aes(x = EUNISa_1_descr, fill = CH_cat)) +
geom_bar() + theme_bw() + coord_flip() +
scale_y_continuous(labels = label_number()) +
scale_fill_viridis_d(direction = -1) +
labs(x = "EUNIS level 1", fill = "Canopy height") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
theme(legend.position = c(0.8, 0.75),
legend.direction = "vertical")

Stats per habitat type
data_validation %>%
# Keep only forests, grasslands, shrublands and wetlands
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
group_by(EUNISa_1_descr) %>%
summarise(across(canopy_height, list(
mean = mean,
median = median,
sd = sd,
min = min,
max = max
), na.rm = TRUE))
Phenology
Only using NDVI- and SAVI-based values so far.
Maximum NDVI should be equal to value at peak?
nrow(data_validation %>% dplyr::filter(NDVI_pos_value != NDVI_max))
[1] 403
Not sure why this happens, but in most cases the difference is small
(< -0.1). Anyway, we should use only one of these two in the RF
models.
plot_histogram(data_validation, "NDVI_sos_doy", "NDVI_sos_doy")

plot_histogram(data_validation, "NDVI_pos_doy", "NDVI_pos_doy")

plot_histogram(data_validation, "NDVI_eos_doy", "NDVI_eos_doy")

plot_histogram(data_validation, "EVI_sos_doy", "EVI_sos_doy")

plot_histogram(data_validation, "EVI_pos_doy", "EVI_pos_doy")

plot_histogram(data_validation, "EVI_eos_doy", "EVI_eos_doy")

plot_histogram(data_validation, "SAVI_sos_doy", "SAVI_sos_doy")

plot_histogram(data_validation, "SAVI_pos_doy", "SAVI_pos_doy")

plot_histogram(data_validation, "SAVI_eos_doy", "SAVI_eos_doy")

plot_histogram(data_validation, "NDVI_sos_value", "NDVI_sos_value")

plot_histogram(data_validation, "NDVI_pos_value", "NDVI_pos_value")

plot_histogram(data_validation, "NDVI_eos_value", "NDVI_eos_value")

plot_histogram(data_validation, "EVI_sos_value", "EVI_sos_value")

plot_histogram(data_validation, "EVI_pos_value", "EVI_pos_value")

plot_histogram(data_validation, "EVI_eos_value", "EVI_eos_value")

plot_histogram(data_validation, "NDVI_gsd", "NDVI_gsd")

plot_histogram(data_validation, "EVI_gsd", "EVI_gsd")

plot_histogram(data_validation, "SAVI_gsd", "SAVI_gsd")

plot_histogram(data_validation, "NDVI_diff_pos_sos_value",
"NDVI_diff_pos_sos_value")

plot_histogram(data_validation, "EVI_diff_pos_sos_value",
"EVI_diff_pos_sos_value")

plot_histogram(data_validation, "SAVI_diff_pos_sos_value",
"SAVI_diff_pos_sos_value")

plot_histogram(data_validation, "NDVI_diff_pos_eos_value",
"NDVI_diff_pos_eos_value")

plot_histogram(data_validation, "EVI_diff_pos_eos_value",
"EVI_diff_pos_eos_value")

plot_histogram(data_validation, "SAVI_diff_pos_eos_value",
"SAVI_diff_pos_eos_value")

plot_histogram(data_validation, "NDVI_diff_pos_sos_doy",
"NDVI_diff_pos_sos_doy")

plot_histogram(data_validation, "EVI_diff_pos_sos_doy",
"EVI_diff_pos_sos_doy")

plot_histogram(data_validation, "SAVI_diff_pos_sos_doy",
"SAVI_diff_pos_sos_doy")

plot_histogram(data_validation, "NDVI_diff_eos_pos_doy",
"NDVI_diff_eos_pos_doy")

plot_histogram(data_validation, "EVI_diff_eos_pos_doy",
"EVI_diff_eos_pos_doy")

plot_histogram(data_validation, "SAVI_diff_eos_pos_doy",
"SAVI_diff_eos_pos_doy")

plot_histogram(data_validation, "NDVI_auc", "NDVI_auc")

plot_histogram(data_validation, "EVI_auc", "EVI_auc")

plot_histogram(data_validation, "SAVI_auc", "SAVI_auc")

distr_plot(data_validation,
c("NDVI_sos_value","NDVI_pos_value", "NDVI_eos_value",
"EVI_sos_value","EVI_pos_value", "EVI_eos_value",
"SAVI_sos_value", "SAVI_pos_value", "SAVI_eos_value",
"NDVI_sos_doy","NDVI_pos_doy", "NDVI_eos_doy",
"EVI_sos_doy","EVI_pos_doy", "EVI_eos_doy",
"SAVI_sos_doy", "SAVI_pos_doy", "SAVI_eos_doy"),
c("NDVI_sos_value","NDVI_pos_value", "NDVI_eos_value",
"EVI_sos_value","EVI_pos_value", "EVI_eos_value",
"SAVI_sos_Value", "SAVI_pos_value", "SAVI_eos_value",
"NDVI_sos_doy","NDVI_pos_doy", "NDVI_eos_doy",
"EVI_sos_doy","EVI_pos_doy", "EVI_eos_doy",
"SAVI_sos_doy", "SAVI_pos_doy", "SAVI_eos_doy")
)


















distr_plot(data_validation,
c("NDVI_gsd","EVI_gsd", "SAVI_gsd",
"NDVI_diff_pos_sos_value", "EVI_diff_pos_sos_value",
"SAVI_diff_pos_sos_value",
"NDVI_diff_pos_eos_value", "EVI_diff_pos_eos_value",
"SAVI_diff_pos_eos_value",
"NDVI_diff_pos_sos_doy", "EVI_diff_pos_sos_doy",
"SAVI_diff_pos_sos_doy",
"NDVI_diff_eos_pos_doy", "EVI_diff_eos_pos_doy",
"SAVI_diff_eos_pos_doy"),
c("NDVI_gsd","EVI_gsd", "SAVI_gsd",
"NDVI_diff_pos_sos_value", "EVI_diff_pos_sos_value",
"SAVI_diff_pos_sos_Value",
"NDVI_diff_pos_eos_value", "EVI_diff_pos_eos_value",
"SAVI_diff_pos_eos_value",
"NDVI_diff_pos_sos_doy", "EVI_diff_pos_sos_doy",
"SAVI_diff_pos_sos_doy",
"NDVI_diff_eos_pos_doy", "EVI_diff_eos_pos_doy",
"SAVI_diff_eos_pos_doy")
)















distr_plot(data_validation,
c("NDVI_auc", "EVI_auc", "SAVI_auc"),
c("NDVI_auc", "EVI_auc", "SAVI_auc"))



RF models
vars_RF <- c(
# Min values of all indices
"NDVI_min", "EVI_min", "NDMI_min", "NDWI_min", "SAVI_min",
# Max values of NDMI and NDWI
"NDMI_max", "NDWI_max",
# AUC of NDVI, EVI and SAVI
"NDVI_auc", "EVI_auc", "SAVI_auc",
# Values of NDVI, EVI and SAVI at sos, pos and eos
"NDVI_sos_value", "NDVI_pos_value", "NDVI_eos_value",
"EVI_sos_value", "EVI_pos_value", "EVI_eos_value",
"SAVI_sos_value", "SAVI_pos_value", "SAVI_eos_value",
# Differences pos-sos in value and doy
"NDVI_diff_pos_sos_value", "EVI_diff_pos_sos_value", "SAVI_diff_pos_sos_value",
"NDVI_diff_pos_sos_doy", "EVI_diff_pos_sos_doy", "SAVI_diff_pos_sos_doy",
# Differences pos-eos in value and doy
"NDVI_diff_pos_eos_value", "EVI_diff_pos_eos_value","SAVI_diff_pos_eos_value",
"NDVI_diff_eos_pos_doy", "EVI_diff_eos_pos_doy", "SAVI_diff_eos_pos_doy",
# Growing season duration
"NDVI_gsd", "EVI_gsd", "SAVI_gsd",
# Canopy height
"canopy_height")
RF with all points
filtered_data0 <- data_validation %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
# Remove all rows with wrong values of indices (not between -1 and 1)
dplyr::filter(EVI_max <= 1 & EVI_min >= -1) %>%
dplyr::filter(NDVI_max <= 1) %>%
dplyr::filter(NDMI_max <= 1) %>%
dplyr::filter(NDWI_min >= -1) %>%
# Remove rows with missing values
dplyr::filter(if_all(all_of(vars_RF), ~ !is.na(.))) %>%
# Keep only rows with differences > 0
dplyr::filter(if_all(contains("diff"), ~ .x > 0)) %>%
# Select only variables needed
select(EUNISa_1, all_of(vars_RF))
Correlation of all variables to be included in RF models:
corrplot(filtered_data0 %>%
select(all_of(vars_RF)) %>%
cor(use = "pairwise.complete.obs"),
method = "color", type = "upper", tl.col = "black", tl.srt = 45)

Split into training and test data sets.
train_indices0 <- sample(1:nrow(filtered_data0), 0.7 * nrow(filtered_data0))
train_data0 <- filtered_data0[train_indices0, ]
test_data0 <- filtered_data0[-train_indices0, ]
Number of points per category for filtered data:
filtered_data0 %>% count(EUNISa_1)
rf_cforest0 <- party::cforest(
formula = reformulate(vars_RF, response = "EUNISa_1"),
data = train_data0,
controls = cforest_control(mtry = round(sqrt(length(all_of(vars_RF)))),
# mtry = sqrt(35) # sqrt of total n variables
# Default mtry = 5
# Bagging: mtry = NULL
# or = number of input variables
ntree = 500) # Default, try increasing
)
predictions_rf_cforest0 <- predict(rf_cforest0, newdata = test_data0,
OOB = TRUE, type = "response")
beep()
Confusion matrix:
confusionMatrix(predictions_rf_cforest0, test_data0$EUNISa_1)
Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 113 17 10 1
R 378 2114 320 112
S 77 95 570 27
T 26 64 33 608
Overall Statistics
Accuracy : 0.7459
95% CI : (0.733, 0.7585)
No Information Rate : 0.5016
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.5861
Mcnemar's Test P-Value : < 2.2e-16
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.19024 0.9231 0.6109 0.8128
Specificity 0.99295 0.6440 0.9452 0.9678
Pos Pred Value 0.80142 0.7230 0.7412 0.8317
Neg Pred Value 0.89127 0.8927 0.9044 0.9635
Prevalence 0.13012 0.5016 0.2044 0.1639
Detection Rate 0.02475 0.4631 0.1249 0.1332
Detection Prevalence 0.03089 0.6405 0.1685 0.1601
Balanced Accuracy 0.59159 0.7836 0.7781 0.8903
varimp_rf_cforest0 <- party::varimp(rf_cforest0, conditional = F)
Variable Importance Plot
varimp_rf_cforest0_df <- data.frame(Variable = names(varimp_rf_cforest0),
Importance = varimp_rf_cforest0)
ggplot(varimp_rf_cforest0_df,
aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "lightblue") +
coord_flip() + theme_minimal() +
labs(title = "Variable Importance", x = "Variables", y = "Importance")

ROC curves:
# Predict probabilities for each class
probabilities <- predict(rf_cforest0, newdata = test_data0, type = "prob")
# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T") # Adjust if needed
prob_df <- as.data.frame(prob_matrix)
# Step 2: Prepare actual class labels
actual <- factor(test_data0$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)
# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
G3;Setting levels: control = 0, case = 1
gG3;Setting direction: controls < cases
gG3;Setting levels: control = 0, case = 1
gG3;Setting direction: controls < cases
gG3;Setting levels: control = 0, case = 1
gG3;Setting direction: controls < cases
gG3;Setting levels: control = 0, case = 1
gG3;Setting direction: controls < cases
g
# Step 5: Plot ROC curves with ggplot2
roc0 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) +
geom_abline(linetype = "dashed", color = "gray") +
labs(
title = "Multiclass ROC Curves with AUC",
x = "False Positive Rate",
y = "True Positive Rate",
color = "Class (AUC)"
) +
theme_minimal() +
theme(legend.position = "bottom")
roc0

RF with all GPS points (diff or not)
filtered_data1 <- data_validation %>%
# Select only GPS points
dplyr::filter(`Location method` == "Location with GPS" |
`Location method` == "Location with differential GPS") %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
# Remove all rows with wrong values of indices (not between -1 and 1)
dplyr::filter(EVI_max <= 1 & EVI_min >= -1) %>%
dplyr::filter(NDVI_max <= 1) %>%
dplyr::filter(NDMI_max <= 1) %>%
dplyr::filter(NDWI_min >= -1) %>%
# Remove rows with missing values
dplyr::filter(if_all(all_of(vars_RF), ~ !is.na(.))) %>%
# Keep only rows with differences > 0
dplyr::filter(if_all(contains("diff"), ~ .x > 0)) %>%
# Select only variables needed
select(EUNISa_1, all_of(vars_RF))
Split into training and test data sets.
train_indices1 <- sample(1:nrow(filtered_data1), 0.7 * nrow(filtered_data1))
train_data1 <- filtered_data1[train_indices1, ]
test_data1 <- filtered_data1[-train_indices1, ]
Number of points per category for filtered data:
filtered_data1 %>% count(EUNISa_1)
rf_cforest1 <- party::cforest(
formula = reformulate(vars_RF, response = "EUNISa_1"),
data = train_data1,
controls = cforest_control(mtry = round(sqrt(length(all_of(vars_RF)))),
# mtry = sqrt(35) # sqrt of total n variables
# Default mtry = 5
# Bagging: mtry = NULL
# or = number of input variables
ntree = 500) # Default, try increasing
)
beep()
predictions_rf_cforest1 <- predict(rf_cforest1, newdata = test_data1,
OOB = TRUE, type = "response")
Confusion matrix:
confusionMatrix(predictions_rf_cforest1, test_data1$EUNISa_1)
Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 158 21 29 0
R 238 1106 156 66
S 96 107 522 13
T 12 27 10 110
Overall Statistics
Accuracy : 0.7098
95% CI : (0.6922, 0.727)
No Information Rate : 0.4721
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.5395
Mcnemar's Test P-Value : < 2.2e-16
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.31349 0.8771 0.7280 0.58201
Specificity 0.97693 0.6738 0.8895 0.98026
Pos Pred Value 0.75962 0.7063 0.7073 0.69182
Neg Pred Value 0.85952 0.8597 0.8991 0.96855
Prevalence 0.18869 0.4721 0.2684 0.07076
Detection Rate 0.05915 0.4141 0.1954 0.04118
Detection Prevalence 0.07787 0.5863 0.2763 0.05953
Balanced Accuracy 0.64521 0.7754 0.8087 0.78113
varimp_rf_cforest1 <- party::varimp(rf_cforest1, conditional = F)
Variable Importance Plot
varimp_rf_cforest1_df <- data.frame(Variable = names(varimp_rf_cforest1),
Importance = varimp_rf_cforest1)
ggplot(varimp_rf_cforest1_df,
aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "lightblue") +
coord_flip() + theme_minimal() +
labs(title = "Variable Importance", x = "Variables", y = "Importance")

ROC curves:
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")
# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T") # Adjust if needed
prob_df <- as.data.frame(prob_matrix)
# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)
# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
G3;Setting levels: control = 0, case = 1
gG3;Setting direction: controls < cases
gG3;Setting levels: control = 0, case = 1
gG3;Setting direction: controls < cases
gG3;Setting levels: control = 0, case = 1
gG3;Setting direction: controls < cases
gG3;Setting levels: control = 0, case = 1
gG3;Setting direction: controls < cases
g
# Step 5: Plot ROC curves with ggplot2
roc1 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) +
geom_abline(linetype = "dashed", color = "gray") +
labs(
title = "Multiclass ROC Curves with AUC",
x = "False Positive Rate",
y = "True Positive Rate",
color = "Class (AUC)"
) +
theme_minimal() +
theme(legend.position = "bottom")
roc1

RF with …
First validation
For T, R, S, Q habitats.
Define a set of rules for a first validation of ALL ReSurvey data. We
can call these “Expert-based” rules.
Number of observations in ReSurvey from the habitats of interest:
nrow(data_validation %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")))
Number of observations in ReSurvey from the habitats of interest and
with all RS data:
nrow(data_validation %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
dplyr::filter(CH_data == T) %>%
dplyr::filter(RS_data ==T) %>%
dplyr::filter(RS_phen_data == T))
data_validation_terrestrial <- data_validation %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q"))
Define rules
Create column for first validation based on different indicators,
where “wrong” is noted when the validation rule is not met. Include
EUNIS1 confusions.
data_validation_terrestrial %>% count(EUNISa_1, EUNIS1_conf_type)
Define rules:
data_validation_terrestrial <-
data_validation_terrestrial %>%
mutate(
valid_1_NDWI = case_when(
# Points that are basically water
NDWI_max > 0.3 ~ "wrong",
TRUE ~ NA_character_),
valid_1_CH = case_when(
# T points with low CH
EUNISa_1 == "T" & canopy_height < 8 ~ "wrong",
# S points with low CH
EUNISa_1 =="S" & canopy_height < 5 ~ "wrong",
# R & Q points with high CH
EUNISa_1 %in% c("R", "Q") & canopy_height > 2 ~ "wrong",
TRUE ~ NA_character_),
valid_1_NDVI = case_when(
# T points with low NDVI_max
EUNISa_1 == "T" & NDVI_max < 0.6 ~ "wrong",
# S-R-Q points with low NDVI_max
EUNISa_1 %in% c("R", "S", "Q") & NDVI_max < 0.2 ~ "wrong",
TRUE ~ NA_character_),
# Count how many validation rules are not met
valid_1_count = rowSums(across(c(valid_1_NDWI, valid_1_CH, valid_1_NDVI),
~ . == "wrong"), na.rm = TRUE),
# Points where at least 1 rule not met
valid_1 = if_else(valid_1_count > 0, "At least 1 rule broken",
"No rules broken so far")
)
Plots first validation
ggplot(data_validation_terrestrial%>%
mutate(rules_broken = case_when(
valid_1_count == 1 & valid_1_NDWI == "wrong" ~ "NDWI",
valid_1_count == 1 & valid_1_NDVI == "wrong" ~ "NDVI",
valid_1_count == 1 & valid_1_CH == "wrong" ~ "CH",
valid_1_count == 2 &
valid_1_NDWI == "wrong" & valid_1_NDVI == "wrong"~ "NDWI + NDVI",
valid_1_count == 2 &
valid_1_NDWI == "wrong" & valid_1_CH == "wrong"~ "NDWI + CH",
valid_1_count == 2 &
valid_1_NDVI == "wrong" & valid_1_CH == "wrong"~ "NDVI + CH",
valid_1_count == 3 ~ "NDWI + NDVI + CH",
TRUE ~ NA_character_
)),
aes(x = valid_1_count, fill = rules_broken)) +
geom_bar() + labs(x = "Number of broken rules")
data_validation_terrestrial %>%
mutate(rules_broken = case_when(
valid_1_count == 1 & valid_1_NDWI == "wrong" ~ "NDWI",
valid_1_count == 1 & valid_1_NDVI == "wrong" ~ "NDVI",
valid_1_count == 1 & valid_1_CH == "wrong" ~ "CH",
valid_1_count == 2 &
valid_1_NDWI == "wrong" & valid_1_NDVI == "wrong"~ "NDWI + NDVI",
valid_1_count == 2 &
valid_1_NDWI == "wrong" & valid_1_CH == "wrong"~ "NDWI + CH",
valid_1_count == 2 &
valid_1_NDVI == "wrong" & valid_1_CH == "wrong"~ "NDVI + CH",
valid_1_count == 3 ~ "NDWI + NDVI + CH",
TRUE ~ NA_character_
)) %>%
count(rules_broken, EUNIS1_conf_type)
Proportion of observations not validated (so far):
nrow(data_validation_terrestrial %>% dplyr::filter(valid_1_count > 0))/
nrow(data_validation_terrestrial)
But be aware that there are still MANY missing RS data.
ggplot(data_validation_terrestrial %>%
mutate(diff_GPS = if_else(
`Location method` != "Location with differential GPS" |
is.na(`Location method`), "no", "yes")),
aes(x = diff_GPS, fill = valid_1)) +
geom_bar() + labs(x = "Differential GPS")
ggplot(data_validation_terrestrial %>%
mutate(GPS = case_when(
`Location method` == "Location with differential GPS" ~ "yes",
`Location method` == "Location with GPS" ~ "yes",
is.na(`Location method`) ~ "no",
TRUE ~ "no"
)),
aes(x = GPS, fill = valid_1)) +
geom_bar() + labs(x = "GPS")
Points with any rule broken and confusion between EUNIS:
nrow(data_validation_terrestrial %>%
dplyr::filter(EUNIS1_conf == T & valid_1_count > 0))
Convert to shp to look at these in GIS:
# st_write(data_validation_terrestrial %>%
# dplyr::filter(EUNIS1_conf == T & valid_1_count > 0) %>%
# st_as_sf(coords = c("Lon_updated", "Lat_updated"), crs = 4326),
# "C:/GIS/MOTIVATE/shapefiles/resurv_not_val_EUNIS_conf.shp")
Checked and yes
How many points with differential GPS that have at least 1 rule
broken?
nrow(data_validation_terrestrial %>%
dplyr::filter(`Location method` == "Location with differential GPS" &
valid_1 == "At least 1 rule broken"))
Convert to shp to look at these in GIS:
# st_write(data_validation_terrestrial %>%
# dplyr::filter(`Location method` == "Location with differential GPS" &
# valid_1 == "At least 1 rule broken") %>%
# st_as_sf(coords = c("Lon_updated", "Lat_updated"), crs = 4326),
# "C:/GIS/MOTIVATE/shapefiles/resurv_not_val_diff_GPS.shp")
RF models
Using the conditional inference version of random forest (cforest in
package party). Suggested if the data are highly correlated. Cforest is
more stable in deriving variable importance values in the presence of
highly correlated variables, thus providing better accuracy in
calculating variable importance (ref below).
Hothorn, T., Hornik, K. and Zeileis, A. (2006) Unbiased Recursive
Portioning: A Conditional Inference Framework. Journal of Computational
and Graphical Statistics, 15, 651- 674. http://dx.doi.org/10.1198/106186006X133933
All GPS points
dplyr::filtered_data0 <- all_GPS_valid %>%
dplyr::filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
!is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
!is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
!is.na(EVI_min)) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
dplyr::filter(EVI_max <= 1 & EVI_min >= -1)
Split into training and test data sets.
train_indices0 <- sample(1:nrow(dplyr::filtered_data0), 0.7 * nrow(dplyr::filtered_data0))
train_data0 <- dplyr::filtered_data0[train_indices0, ]
test_data0 <- dplyr::filtered_data0[-train_indices0, ]
Number of points per category for dplyr::filtered data:
dplyr::filtered_data0 %>% count(EUNISa_1)
rf_cforest0 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
NDMI_min + NDWI_max + NDWI_min + EVI_max +
EVI_min + SAVI_max + SAVI_min + canopy_height,
data = train_data0,
controls = cforest_control(
mtry = 3,
# mtry = sqrt(11)
# Default mtry = 5
# Bagging: mtry = NULL
# or = number of input variables
ntree = 500) # Default, try increasing
)
predictions_rf_cforest0 <- predict(rf_cforest0, newdata = test_data0,
OOB = TRUE, type = "response")
Confusion matrix:
confusionMatrix(predictions_rf_cforest0, test_data0$EUNISa_1)
varimp_rf_cforest0 <- party::varimp(rf_cforest0, conditional = F)
Variable Importance Plot
varimp_rf_cforest0_df <- data.frame(Variable = names(varimp_rf_cforest0),
Importance = varimp_rf_cforest0)
ggplot(varimp_rf_cforest0_df,
aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "lightblue") +
coord_flip() + theme_minimal() +
labs(title = "Variable Importance", x = "Variables", y = "Importance")
ROC curves:
# Predict probabilities for each class
probabilities <- predict(rf_cforest0, newdata = test_data0, type = "prob")
# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T") # Adjust if needed
prob_df <- as.data.frame(prob_matrix)
# Step 2: Prepare actual class labels
actual <- factor(test_data0$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)
# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
# Step 5: Plot ROC curves with ggplot2
roc0 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) +
geom_abline(linetype = "dashed", color = "gray") +
labs(
title = "Multiclass ROC Curves with AUC",
x = "False Positive Rate",
y = "True Positive Rate",
color = "Class (AUC)"
) +
theme_minimal() +
theme(legend.position = "bottom")
roc0
REVISE FROM HERE: All GPS points above p20
dplyr::filter the data to get only GPS-points above p20 of NDVI_max
and NDMI_min.
all_GPS_valid <- all_GPS_valid %>%
select(-percentile_20_NDVI_max, -percentile_20_NDMI_min)
percentiles <- all_GPS_valid %>%
group_by(EUNISa_1) %>%
summarize(
percentile_20_NDVI_max = quantile(NDVI_max, 0.20, na.rm = T),
percentile_20_NDMI_min = quantile(NDMI_min, 0.20, na.rm = T),
percentile_80_NDVI_max = quantile(NDVI_max, 0.80, na.rm = T),
percentile_80_NDMI_min = quantile(NDMI_min, 0.80, na.rm = T)
)
# Join the percentiles back to the original data
all_GPS_valid <- all_GPS_valid %>%
left_join(percentiles, by = "EUNISa_1")
# dplyr::filter rows above the 20th percentile for both variables for each category of EUNISa_1
dplyr::filtered_data1 <- all_GPS_valid %>%
dplyr::filter(
NDVI_max >= percentile_20_NDVI_max & NDMI_min >= percentile_20_NDMI_min
) %>%
dplyr::filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
!is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
!is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
!is.na(EVI_min)) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
dplyr::filter(EVI_max <= 1 & EVI_min >= -1)
Split into training and test data sets.
train_indices1 <- sample(1:nrow(dplyr::filtered_data1), 0.7 * nrow(dplyr::filtered_data1))
train_data1 <- dplyr::filtered_data1[train_indices1, ]
test_data1 <- dplyr::filtered_data1[-train_indices1, ]
Number of points per category for dplyr::filtered data:
dplyr::filtered_data1 %>% count(EUNISa_1)
Investigate package ggparty (e.g. autoplot function, and more).
TO-DO: Choose the hyperparameter mtry based on the square root of the
number of predictor variables (Hastie et al., 2009)-
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements
of statistical learning: Data mining, inference, and prediction.
Springer Science & Business Media.
Maybe TO_DO: We variated ntree from 50 to 800 in steps of 50, leaving
mtry constant at 2. Tis parameter variation showed that ntree=500 was
optimal, while higher ntree led to no further model improvement
(Supplementary Fig. S10). Subsequently, the hyperparameter mtry was
varied from 2 to 8 with constant ntree=500. Here, mtry=3 led to the best
results in almost all cases (Supplementary Fig. S11). Consequently, we
chose ntree=500 and mtry=3 for our main analysis across all study
sites.
rf_cforest1 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
NDMI_min + NDWI_max + NDWI_min + EVI_max +
EVI_min + SAVI_max + SAVI_min + canopy_height,
data = train_data1,
controls = cforest_control(
mtry = 3,
# mtry = sqrt(11)
# Default mtry = 5
# Bagging: mtry = NULL
# or = number of input variables
ntree = 500) # Default, try increasing
)
predictions_rf_cforest1 <- predict(rf_cforest1, newdata = test_data1,
OOB = TRUE, type = "response")
Confusion matrix:
confusionMatrix(predictions_rf_cforest1, test_data1$EUNISa_1)
SurrogateTree –> does not work
varimp_rf_cforest1 <- party::varimp(rf_cforest1, conditional = F)
Variable Importance Plot
varimp_rf_cforest1_df <- data.frame(Variable = names(varimp_rf_cforest1),
Importance = varimp_rf_cforest1)
ggplot(varimp_rf_cforest1_df,
aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "lightblue") +
coord_flip() + theme_minimal() +
labs(title = "Variable Importance", x = "Variables", y = "Importance")
Tree Visualization
# Create a single conditional inference tree using ctree
single_tree1 <- ctree(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max + NDMI_min +
NDWI_max + NDWI_min + EVI_max + EVI_min + SAVI_max +
SAVI_min + canopy_height,
data = train_data1)
# Plot the single tree using
autoplot(single_tree1)
ROC curves:
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")
# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T") # Adjust if needed
prob_df <- as.data.frame(prob_matrix)
# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)
# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
# Step 5: Plot ROC curves with ggplot2
roc1 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) +
geom_abline(linetype = "dashed", color = "gray") +
labs(
title = "Multiclass ROC Curves with AUC",
x = "False Positive Rate",
y = "True Positive Rate",
color = "Class (AUC)"
) +
theme_minimal() +
theme(legend.position = "bottom")
roc1
All GPS points within IQ range
dplyr::filter the data to get only GPS-points within IQ range of
NDVI_max and NDMI_min.
IQ_ranges <- all_GPS_valid %>%
group_by(EUNISa_1) %>%
summarize(
Q1_NDVI_max = quantile(NDVI_max, 0.25, na.rm = T),
Q1_NDMI_min = quantile(NDMI_min, 0.25, na.rm = T),
Q3_NDVI_max = quantile(NDVI_max, 0.75, na.rm = T),
Q3_NDMI_min = quantile(NDMI_min, 0.75, na.rm = T),
IQR_NDVI_max = IQR(NDVI_max, na.rm = TRUE),
IQR_NDMI_min = IQR(NDMI_min, na.rm = TRUE)
)
# Join the IQ ranges back to the original data
all_GPS_valid <- all_GPS_valid %>%
left_join(IQ_ranges, by = "EUNISa_1")
# Filter rows within the IQR range for both variables
dplyr::filtered_data2 <- all_GPS_valid %>%
dplyr::filter(
(NDVI_max >= Q1_NDVI_max & NDVI_max <= Q3_NDVI_max) &
(NDMI_min >= Q1_NDMI_min & NDMI_min <= Q3_NDMI_min)
) %>%
dplyr::filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
!is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
!is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
!is.na(EVI_min)) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
dplyr::filter(EVI_max <= 1 & EVI_min >= -1)
Split into training and test data sets.
train_indices2 <- sample(1:nrow(filtered_data2), 0.7 * nrow(filtered_data2))
train_data2 <- filtered_data2[train_indices2, ]
test_data2 <- filtered_data2[-train_indices2, ]
Number of points per category for filtered data:
filtered_data2 %>% count(EUNISa_1)
rf_cforest2 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
NDMI_min + NDWI_max + NDWI_min + EVI_max +
EVI_min + SAVI_max + SAVI_min + canopy_height,
data = train_data2,
controls = cforest_control(
mtry = 3,
# mtry = sqrt(11)
# Default mtry = 5
# Bagging: mtry = NULL
# or = number of input variables
ntree = 500) # Default, try increasing
)
predictions_rf_cforest2 <- predict(rf_cforest2, newdata = test_data2,
OOB = TRUE, type = "response")
Confusion matrix:
confusionMatrix(predictions_rf_cforest2, test_data2$EUNISa_1)
varimp_rf_cforest2 <- party::varimp(rf_cforest2, conditional = F)
Variable Importance Plot
varimp_rf_cforest2_df <- data.frame(Variable = names(varimp_rf_cforest2),
Importance = varimp_rf_cforest2)
ggplot(varimp_rf_cforest2_df,
aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "lightblue") +
coord_flip() + theme_minimal() +
labs(title = "Variable Importance", x = "Variables", y = "Importance")
ROC curves:
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")
# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T") # Adjust if needed
prob_df <- as.data.frame(prob_matrix)
# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)
# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
# Step 5: Plot ROC curves with ggplot2
roc2 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) +
geom_abline(linetype = "dashed", color = "gray") +
labs(
title = "Multiclass ROC Curves with AUC",
x = "False Positive Rate",
y = "True Positive Rate",
color = "Class (AUC)"
) +
theme_minimal() +
theme(legend.position = "bottom")
roc2
All GPS points within 1.5 * IQ range
Filter the data to get only GPS-points within 1.5 * IQ range of
NDVI_max and NDMI_min.
# Filter rows within the 1.5 * IQR range for both variables
filtered_data3 <- all_GPS_valid %>%
filter(
(NDVI_max >= (Q1_NDVI_max - 1.5 * IQR_NDVI_max) & NDVI_max <= (Q3_NDVI_max + 1.5 * IQR_NDVI_max)) &
(NDMI_min >= (Q1_NDMI_min - 1.5 * IQR_NDMI_min) & NDMI_min <= (Q3_NDMI_min + 1.5 * IQR_NDMI_min))
) %>%
filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
!is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
!is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
!is.na(EVI_min)) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
filter(EVI_max <= 1 & EVI_min >= -1)
Split into training and test data sets.
train_indices3 <- sample(1:nrow(filtered_data3), 0.7 * nrow(filtered_data3))
train_data3 <- filtered_data3[train_indices3, ]
test_data3 <- filtered_data3[-train_indices3, ]
Number of points per category for filtered data:
filtered_data3 %>% count(EUNISa_1)
rf_cforest3 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
NDMI_min + NDWI_max + NDWI_min + EVI_max +
EVI_min + SAVI_max + SAVI_min + canopy_height,
data = train_data3,
controls = cforest_control(
mtry = 3,
# mtry = sqrt(11)
# Default mtry = 5
# Bagging: mtry = NULL
# or = number of input variables
ntree = 500) # Default, try increasing
)
predictions_rf_cforest3 <- predict(rf_cforest3, newdata = test_data3,
OOB = TRUE, type = "response")
Confusion matrix:
confusionMatrix(predictions_rf_cforest3, test_data3$EUNISa_1)
varimp_rf_cforest3 <- party::varimp(rf_cforest3, conditional = F)
Variable Importance Plot
varimp_rf_cforest3_df <- data.frame(Variable = names(varimp_rf_cforest3),
Importance = varimp_rf_cforest3)
ggplot(varimp_rf_cforest3_df,
aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "lightblue") +
coord_flip() + theme_minimal() +
labs(title = "Variable Importance", x = "Variables", y = "Importance")
ROC curves:
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")
# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T") # Adjust if needed
prob_df <- as.data.frame(prob_matrix)
# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)
# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
# Step 5: Plot ROC curves with ggplot2
roc3 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) +
geom_abline(linetype = "dashed", color = "gray") +
labs(
title = "Multiclass ROC Curves with AUC",
x = "False Positive Rate",
y = "True Positive Rate",
color = "Class (AUC)"
) +
theme_minimal() +
theme(legend.position = "bottom")
roc3
All GPS points within mean +/- SD
Filter the data to get only GPS-points within mean +/- SD of NDVI_max
and NDMI_min.
mean_sd <- all_GPS_valid %>%
group_by(EUNISa_1) %>%
summarize(
mean_NDVI_max = mean(all_GPS_valid$NDVI_max, na.rm = T),
mean_NDMI_min = mean(all_GPS_valid$NDMI_min, na.rm = T),
sd_NDVI_max = sd(all_GPS_valid$NDVI_max, na.rm = T),
sd_NDMI_min = sd(all_GPS_valid$NDMI_min, na.rm = T)
)
# Join the IQ ranges back to the original data
all_GPS_valid <- all_GPS_valid %>%
left_join(mean_sd, by = "EUNISa_1")
# Filter rows within the specified range for both variables
filtered_data4 <- all_GPS_valid %>%
filter(
(NDVI_max >= (mean_NDVI_max - sd_NDVI_max) & NDVI_max <= (mean_NDVI_max + sd_NDVI_max)) &
(NDMI_min >= (mean_NDMI_min - sd_NDMI_min) & NDMI_min <= (mean_NDMI_min + sd_NDMI_min))
) %>%
filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
!is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
!is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
!is.na(EVI_min)) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
filter(EVI_max <= 1 & EVI_min >= -1)
Split into training and test data sets.
train_indices4 <- sample(1:nrow(filtered_data4), 0.7 * nrow(filtered_data4))
train_data4 <- filtered_data4[train_indices4, ]
test_data4 <- filtered_data4[-train_indices4, ]
Number of points per category for filtered data:
filtered_data4 %>% count(EUNISa_1)
rf_cforest4 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
NDMI_min + NDWI_max + NDWI_min + EVI_max +
EVI_min + SAVI_max + SAVI_min + canopy_height,
data = train_data4,
controls = cforest_control(
mtry = 3,
# mtry = sqrt(11)
# Default mtry = 5
# Bagging: mtry = NULL
# or = number of input variables
ntree = 500) # Default, try increasing
)
predictions_rf_cforest4 <- predict(rf_cforest4, newdata = test_data4,
OOB = TRUE, type = "response")
Confusion matrix:
confusionMatrix(predictions_rf_cforest4, test_data4$EUNISa_1)
varimp_rf_cforest4 <- party::varimp(rf_cforest4, conditional = F)
Variable Importance Plot
varimp_rf_cforest4_df <- data.frame(Variable = names(varimp_rf_cforest4),
Importance = varimp_rf_cforest4)
ggplot(varimp_rf_cforest4_df,
aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "lightblue") +
coord_flip() + theme_minimal() +
labs(title = "Variable Importance", x = "Variables", y = "Importance")
ROC curves:
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")
# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T") # Adjust if needed
prob_df <- as.data.frame(prob_matrix)
# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)
# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
# Step 5: Plot ROC curves with ggplot2
roc4 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) +
geom_abline(linetype = "dashed", color = "gray") +
labs(
title = "Multiclass ROC Curves with AUC",
x = "False Positive Rate",
y = "True Positive Rate",
color = "Class (AUC)"
) +
theme_minimal() +
theme(legend.position = "bottom")
roc4
All GPS points above p20 and below p80
Filter the data to get only GPS-points above p20 and below p80 of
NDVI_max and NDMI_min.
# Filter rows above the 20th percentile and below the 80th percentile for both variables
filtered_data5 <- all_GPS_valid %>%
filter(
(NDVI_max >= percentile_20_NDVI_max & NDVI_max <= percentile_80_NDVI_max) &
(NDMI_min >= percentile_20_NDMI_min & NDMI_min <= percentile_80_NDMI_min)
) %>%
filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
!is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
!is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
!is.na(EVI_min)) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
filter(EVI_max <= 1 & EVI_min >= -1)
Split into training and test data sets.
train_indices5 <- sample(1:nrow(filtered_data5), 0.7 * nrow(filtered_data5))
train_data5 <- filtered_data5[train_indices5, ]
test_data5 <- filtered_data5[-train_indices5, ]
Number of points per category for filtered data:
filtered_data5 %>% count(EUNISa_1)
rf_cforest5 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
NDMI_min + NDWI_max + NDWI_min + EVI_max +
EVI_min + SAVI_max + SAVI_min + canopy_height,
data = train_data5,
controls = cforest_control(
mtry = 3,
# mtry = sqrt(11)
# Default mtry = 5
# Bagging: mtry = NULL
# or = number of input variables
ntree = 500) # Default, try increasing
)
predictions_rf_cforest5 <- predict(rf_cforest5, newdata = test_data5,
OOB = TRUE, type = "response")
Confusion matrix:
confusionMatrix(predictions_rf_cforest5, test_data5$EUNISa_1)
varimp_rf_cforest5 <- party::varimp(rf_cforest5, conditional = F)
Variable Importance Plot
varimp_rf_cforest5_df <- data.frame(Variable = names(varimp_rf_cforest5),
Importance = varimp_rf_cforest5)
ggplot(varimp_rf_cforest5_df,
aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "lightblue") +
coord_flip() + theme_minimal() +
labs(title = "Variable Importance", x = "Variables", y = "Importance")
ROC curves:
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")
# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T") # Adjust if needed
prob_df <- as.data.frame(prob_matrix)
# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)
# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
# Step 5: Plot ROC curves with ggplot2
roc5 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) +
geom_abline(linetype = "dashed", color = "gray") +
labs(
title = "Multiclass ROC Curves with AUC",
x = "False Positive Rate",
y = "True Positive Rate",
color = "Class (AUC)"
) +
theme_minimal() +
theme(legend.position = "bottom")
roc5
Cordillera data
AlpineGrasslands_indices <- read_csv(
"C:/Data/MOTIVATE/Cordillera/AlpineGrasslands/AlpineGrassland_Sentinel_Plot_Allyear_Allmetrics.csv")
AlpineGrasslands_phen <- read_csv(
"C:/Data/MOTIVATE/Cordillera/AlpineGrasslands/AlpineGrasslands_Phenology_SOS_EOS_Peak_NDVI_Amplitude.csv")
AlpineGrasslands_CH <- read_csv(
"C:/Data/MOTIVATE/Cordillera/AlpineGrasslands/AlpineGrasslands_CanopyHeight_1m.csv")
VegetationTypes_indices <- read_csv(
"C:/Data/MOTIVATE/Cordillera/VegetationTypes/VegetationTypes_Sentinel_Plot_AllYear_Allmetrics.csv")
VegetationTypes_phen <- read_csv(
"C:/Data/MOTIVATE/Cordillera/VegetationTypes/VegetationTypes_Phenology_SOS_EOS_Peak_NDVI_Amplitude.csv")
VegetationTypes_CH <- read_csv(
"C:/Data/MOTIVATE/Cordillera/VegetationTypes/VegetationTypes_CanopyHeight_1m.csv")
AlpineGrasslands <- AlpineGrasslands_indices %>%
select(-`system:index`, -.geo, -Localidad) %>%
rename(Hábitat = "H�bitat") %>%
full_join(AlpineGrasslands_phen %>%
select(-`system:index`, -.geo, -Localidad) %>%
rename(Hábitat = "H�bitat")) %>%
full_join(AlpineGrasslands_CH %>%
select(-`system:index`, -.geo, -Localidad)) %>%
select(-Date__year, - `Precisi�n`) %>%
mutate(DATE = ymd(DATE)) %>%
rename(ID = "Releve_num") %>%
mutate(ID = as.character(ID)) %>%
mutate(layer = "AlpineGrasslands")
VegetationTypes <- VegetationTypes_indices %>%
select(-`system:index`, -.geo) %>%
full_join(VegetationTypes_phen %>%
select(-`system:index`, -.geo)) %>%
full_join(VegetationTypes_CH %>%
select(-`system:index`, -.geo)) %>%
rename(Hábitat = "TYPE") %>%
mutate(layer = "VegetationTypes")
Merge both datasets:
cordillera <- bind_rows(
AlpineGrasslands %>% select(DATE, ID, starts_with("NDMI"),
starts_with("NDVI"), Hábitat, "EOS_DOY",
"Peak_DOY", "SOS_DOY", "Season_Length",
"canopy_height", "layer"),
VegetationTypes %>% select(DATE, ID, starts_with("NDMI"),
starts_with("NDVI"), Hábitat, "EOS_DOY",
"Peak_DOY", "SOS_DOY", "Season_Length",
"canopy_height", "layer")
) %>%
mutate(EUNISa_1 = case_when(
Hábitat = str_detect(Hábitat, "Pastizal|Cervunal|grassland|meadow") ~ "R",
Hábitat = str_detect(Hábitat, "forest") ~ "T",
Hábitat = str_detect(Hábitat, "Scrub|scrub|Shrubland|shrubland|shrub|Heathland") ~ "S",
Hábitat = str_detect(Hábitat, "Suelo|Scree|scree|cliff") ~ "U",
Hábitat = is.na(Hábitat) ~ "R",
TRUE ~ NA_character_),
EUNISa_1_descr = case_when(
EUNISa_1 == "R" ~ "Grasslands",
EUNISa_1 == "T" ~ "Forests and other wooded land",
EUNISa_1 == "S" ~ "Heathlands, scrub and tundra",
EUNISa_1 == "U" ~ "Inland habitats with no or little soil")
)
NDVI, NDMI
distr_plot(cordillera,
c("NDVI_max", "NDVI_p90", "NDVI_min", "NDVI_p10"),
c("NDVI max", "NDVI p90", "NDVI min", "NDVI p10"))
distr_plot(cordillera,
c("NDMI_max", "NDMI_p90", "NDMI_min", "NDMI_p10"),
c("NDMI max", "NDMI p90", "NDMI min", "NDMI p10"))
---
title: "Script to validate points in ReSurvey database using RS data"
subtitle: "Validation done with a sample of points (last observations)"
author: "Alicia Valdés"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  pdf_document: default
  html_notebook: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE)
```

This R script is used to validate the points in the ReSurvey database using RS indicators (indices + phenology + canopy height).

# Load libraries

```{r}
library(tidyverse)
library(here)
library(gridExtra)
library(readxl)
library(scales)
library(sf)
library(rnaturalearth)
library(dtplyr)
library(lme4)
library(lmerTest)
library(car)
library(ggeffects)
library(party)
library(partykit)
library(strucchange)
library(ggparty)
library(caret)
library(moreparty)
library(randomForest)
library(pROC)
library(corrplot)
```

# Define printall function

```{r}
printall <- function(tibble) {
  print(tibble, width = Inf)
  }
```

# Load geom_flat_violin plot

```{r}
source("https://gist.githubusercontent.com/benmarwick/2a1bb0133ff568cbe28d/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")
```

# Read data

```{r}
data_validation<-read_tsv(here("data", "clean","final_RS_data_bands_S2_sample.csv"))
```

No parsing issues!

# Some data managenemt

## TO-DO: Missing data checks

Do when all RS data is ready!

# Distributions all bioregions

## Indices

```{r}
# Define a function to create histograms
plot_histogram <- function(data, x_var, x_label) {
  ggplot(data %>%
           dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
         aes(x = !!sym(x_var))) +
    geom_histogram(color = "black", fill = "white") +
    labs(x = x_label, y = "Frequency") +
    theme_bw()
}
```

```{r}
# Define a function to create plots with violin + boxplot + points
distr_plot <- function(data, y_vars, y_labels) {
  for (i in seq_along(y_vars)) {
    y_var <- y_vars[[i]]
    y_label <- y_labels[[i]]
    
    p <- ggplot(data = data %>%
                  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
                aes(x = EUNISa_1_descr, y = !!sym(y_var), fill = EUNISa_1_descr)) +
      geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
      geom_point(aes(y = !!sym(y_var), color = EUNISa_1_descr),
                 position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
      geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
      stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
      stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1,
                                                     label = length(x)),
                   geom = "text", aes(label = ..label..), vjust = 0.5) +
      labs(y = y_label, x = "EUNIS level 1") +
      scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
      guides(fill = FALSE, color = FALSE) +
      theme_bw() + coord_flip()
    
    print(p)
  }
}
```

Ranges of min and max:

```{r}
range(data_validation$NDVI_max, na.rm = T) # NDVI_max > 1 (slightly)
range(data_validation$NDMI_max, na.rm = T) # NDMI_max > 1 (slightly)
range(data_validation$NDWI_max, na.rm = T)
range(data_validation$SAVI_max, na.rm = T)
range(data_validation$EVI_max, na.rm = T) # EVI_max > 1 (slightly)
range(data_validation$NDVI_min, na.rm = T)
range(data_validation$NDMI_min, na.rm = T)
range(data_validation$NDWI_min, na.rm = T) # NDWI_min < -1 (slightly)
range(data_validation$SAVI_min, na.rm = T)
range(data_validation$EVI_min, na.rm = T) # EVI_min < -1!
```

```{r}
nrow(data_validation %>% dplyr::filter(NDVI_max > 1))
nrow(data_validation %>% dplyr::filter(NDMI_max > 1))
nrow(data_validation %>% dplyr::filter(EVI_max > 1))
nrow(data_validation %>% dplyr::filter(NDWI_min < -1))
nrow(data_validation %>% dplyr::filter(EVI_min < -1))
```

Histograms to check that max and min values are ok:

```{r}
plot_histogram(data_validation, "NDVI_max", "NDVI max")
plot_histogram(data_validation, "NDMI_max", "NDMI max")
plot_histogram(data_validation, "NDWI_max", "NDWI max")
plot_histogram(data_validation, "SAVI_max", "SAVI max")
plot_histogram(data_validation, "EVI_max", "EVI max")
plot_histogram(data_validation, "NDVI_min", "NDVI min")
plot_histogram(data_validation, "NDMI_min", "NDMI min")
plot_histogram(data_validation, "NDWI_min", "NDWI min")
plot_histogram(data_validation, "SAVI_min", "SAVI min")
plot_histogram(data_validation %>%
                 dplyr::filter(EVI_min > -1.5), "EVI_min", "EVI min")
```

```{r}
nrow(data_validation %>%
       dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
       dplyr::filter(EVI_max > 1 | EVI_max < -1))
data_validation %>%
       dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q"))%>%
  dplyr::filter(EVI_max > 1 | EVI_max < -1) %>%
  count(biogeo, unit)
```

Most EVI values are ok!

Distribution plots:

```{r message=FALSE, warning=FALSE}
distr_plot(data_validation %>% dplyr::filter(EVI_min > -0.5),
           c("NDVI_max", "EVI_max", "SAVI_max", "NDMI_max", "NDWI_max",
             "NDVI_min", "EVI_min", "SAVI_min", "NDMI_min", "NDWI_min"),
           c("NDVI_max", "EVI_max", "SAVI_max", "NDMI_max", "NDWI_max",
             "NDVI_min", "EVI_min", "SAVI_min", "NDMI_min", "NDWI_min"))
```

## CH

```{r}
distr_plot(data_validation, "canopy_height", "Canopy height (m)")
```
 
### Show habitats with CH categories

```{r}
ggplot(data_validation %>%
         # Keep only forests, grasslands, shrublands and wetlands
         dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
         mutate(CH_cat =
                  factor(
                    case_when(canopy_height == 0 ~ "0 m",
                              canopy_height > 0 & canopy_height <= 1 ~ "0-1 m",
                              canopy_height > 1 & canopy_height <=2 ~ "1-2 m",
                              canopy_height > 2 & canopy_height <=5 ~ "2-5 m",
                              canopy_height > 5 & canopy_height <=8 ~ "5-8 m",
                              canopy_height > 8 ~ "> 8 m",
                              is.na(canopy_height) ~ NA_character_),
                    levels = c(
                      "0 m", "0-1 m", "1-2 m", "2-5 m", "5-8 m", "> 8 m"))),
       aes(x = EUNISa_1_descr, fill = CH_cat)) +
  geom_bar() + theme_bw() + coord_flip() +
  scale_y_continuous(labels = label_number()) +
  scale_fill_viridis_d(direction = -1) +
  labs(x = "EUNIS level 1", fill = "Canopy height") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
  theme(legend.position = c(0.8, 0.75),
        legend.direction = "vertical")
```

### Stats per habitat type

```{r}
data_validation %>%
  # Keep only forests, grasslands, shrublands and wetlands
  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
  group_by(EUNISa_1_descr) %>%
  summarise(across(canopy_height, list(
    mean = mean,
    median = median,
    sd = sd,
    min = min,
    max = max
    ), na.rm = TRUE))
```

## Phenology

Only using NDVI- and SAVI-based values so far.

Maximum NDVI should be equal to value at peak?

```{r}
nrow(data_validation %>% dplyr::filter(NDVI_pos_value  != NDVI_max))
```

Not sure why this happens, but in most cases the difference is small (< -0.1). Anyway, we should use only one of these two in the RF models.

```{r}
plot_histogram(data_validation, "NDVI_sos_doy", "NDVI_sos_doy")
plot_histogram(data_validation, "NDVI_pos_doy", "NDVI_pos_doy")
plot_histogram(data_validation, "NDVI_eos_doy", "NDVI_eos_doy")
plot_histogram(data_validation, "EVI_sos_doy", "EVI_sos_doy")
plot_histogram(data_validation, "EVI_pos_doy", "EVI_pos_doy")
plot_histogram(data_validation, "EVI_eos_doy", "EVI_eos_doy")
plot_histogram(data_validation, "SAVI_sos_doy", "SAVI_sos_doy")
plot_histogram(data_validation, "SAVI_pos_doy", "SAVI_pos_doy")
plot_histogram(data_validation, "SAVI_eos_doy", "SAVI_eos_doy")
```

```{r}
plot_histogram(data_validation, "NDVI_sos_value", "NDVI_sos_value")
plot_histogram(data_validation, "NDVI_pos_value", "NDVI_pos_value")
plot_histogram(data_validation, "NDVI_eos_value", "NDVI_eos_value")
plot_histogram(data_validation, "EVI_sos_value", "EVI_sos_value")
plot_histogram(data_validation, "EVI_pos_value", "EVI_pos_value")
plot_histogram(data_validation, "EVI_eos_value", "EVI_eos_value")
```

```{r}
plot_histogram(data_validation, "NDVI_gsd", "NDVI_gsd")
plot_histogram(data_validation, "EVI_gsd", "EVI_gsd")
plot_histogram(data_validation, "SAVI_gsd", "SAVI_gsd")
plot_histogram(data_validation, "NDVI_diff_pos_sos_value",
               "NDVI_diff_pos_sos_value")
plot_histogram(data_validation, "EVI_diff_pos_sos_value",
               "EVI_diff_pos_sos_value")
plot_histogram(data_validation, "SAVI_diff_pos_sos_value",
               "SAVI_diff_pos_sos_value")
plot_histogram(data_validation, "NDVI_diff_pos_eos_value",
               "NDVI_diff_pos_eos_value")
plot_histogram(data_validation, "EVI_diff_pos_eos_value",
               "EVI_diff_pos_eos_value")
plot_histogram(data_validation, "SAVI_diff_pos_eos_value",
               "SAVI_diff_pos_eos_value")
plot_histogram(data_validation, "NDVI_diff_pos_sos_doy",
               "NDVI_diff_pos_sos_doy")
plot_histogram(data_validation, "EVI_diff_pos_sos_doy",
               "EVI_diff_pos_sos_doy")
plot_histogram(data_validation, "SAVI_diff_pos_sos_doy",
               "SAVI_diff_pos_sos_doy")
plot_histogram(data_validation, "NDVI_diff_eos_pos_doy",
               "NDVI_diff_eos_pos_doy")
plot_histogram(data_validation, "EVI_diff_eos_pos_doy", 
               "EVI_diff_eos_pos_doy")
plot_histogram(data_validation, "SAVI_diff_eos_pos_doy", 
               "SAVI_diff_eos_pos_doy")
```

```{r}
plot_histogram(data_validation, "NDVI_auc", "NDVI_auc")
plot_histogram(data_validation, "EVI_auc", "EVI_auc")
plot_histogram(data_validation, "SAVI_auc", "SAVI_auc")
```

```{r}
distr_plot(data_validation,
           c("NDVI_sos_value","NDVI_pos_value", "NDVI_eos_value",
             "EVI_sos_value","EVI_pos_value", "EVI_eos_value",
             "SAVI_sos_value", "SAVI_pos_value", "SAVI_eos_value",
             "NDVI_sos_doy","NDVI_pos_doy", "NDVI_eos_doy",
             "EVI_sos_doy","EVI_pos_doy", "EVI_eos_doy",
             "SAVI_sos_doy", "SAVI_pos_doy", "SAVI_eos_doy"),
           c("NDVI_sos_value","NDVI_pos_value", "NDVI_eos_value",
             "EVI_sos_value","EVI_pos_value", "EVI_eos_value",
             "SAVI_sos_Value", "SAVI_pos_value", "SAVI_eos_value",
             "NDVI_sos_doy","NDVI_pos_doy", "NDVI_eos_doy",
             "EVI_sos_doy","EVI_pos_doy", "EVI_eos_doy",
             "SAVI_sos_doy", "SAVI_pos_doy", "SAVI_eos_doy")
           )
```

```{r}
distr_plot(data_validation,
           c("NDVI_gsd","EVI_gsd", "SAVI_gsd",
             "NDVI_diff_pos_sos_value", "EVI_diff_pos_sos_value",
             "SAVI_diff_pos_sos_value",
             "NDVI_diff_pos_eos_value", "EVI_diff_pos_eos_value",
             "SAVI_diff_pos_eos_value",
             "NDVI_diff_pos_sos_doy", "EVI_diff_pos_sos_doy",
             "SAVI_diff_pos_sos_doy",
             "NDVI_diff_eos_pos_doy", "EVI_diff_eos_pos_doy",
             "SAVI_diff_eos_pos_doy"),
           c("NDVI_gsd","EVI_gsd", "SAVI_gsd",
             "NDVI_diff_pos_sos_value", "EVI_diff_pos_sos_value",
             "SAVI_diff_pos_sos_Value",
             "NDVI_diff_pos_eos_value", "EVI_diff_pos_eos_value",
             "SAVI_diff_pos_eos_value",
             "NDVI_diff_pos_sos_doy", "EVI_diff_pos_sos_doy",
             "SAVI_diff_pos_sos_doy",
             "NDVI_diff_eos_pos_doy", "EVI_diff_eos_pos_doy",
             "SAVI_diff_eos_pos_doy")
           )
```

```{r}
distr_plot(data_validation,
           c("NDVI_auc", "EVI_auc", "SAVI_auc"),
             c("NDVI_auc", "EVI_auc", "SAVI_auc"))
```

# TBD: Distributions per bioregion

```{r}
# Define a function to create plots with violin + boxplot + points
distr_plot_biogeo <- function(data, y_vars, y_labels) {
  plots <- list()
  
  for (i in seq_along(y_vars)) {
    y_var <- y_vars[[i]]
    y_label <- y_labels[[i]]
    
    p <- ggplot(data = data %>%
                  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
                aes(x = EUNISa_1_descr, y = !!sym(y_var), fill = EUNISa_1_descr)) +
      geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
      geom_point(aes(y = !!sym(y_var), color = EUNISa_1_descr),
                 position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
      geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
      stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
      stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1,
                                                     label = length(x)),
                   geom = "text", aes(label = ..label..), vjust = 0.5) +
      labs(y = y_label, x = "EUNISa_1_descr") +
      scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
      guides(fill = FALSE, color = FALSE) +
      theme_bw() + coord_flip() + facet_wrap(~ biogeo)
    
    plots[[y_var]] <- p
  }
  
  return(plots)
}
```

## Indices

Distribution plots:

## CH

```{r}
distr_plot_biogeo(data_validation, "canopy_height", "Canopy height (m)")
```

## Phenology

# RF models

```{r}
vars_RF <- c(
  # Min values of all indices
  "NDVI_min", "EVI_min", "NDMI_min", "NDWI_min", "SAVI_min",
  # Max values of NDMI and NDWI
  "NDMI_max", "NDWI_max",
  # AUC of NDVI, EVI and SAVI
  "NDVI_auc", "EVI_auc", "SAVI_auc",
  # Values of NDVI, EVI and SAVI at sos, pos and eos
  "NDVI_sos_value", "NDVI_pos_value", "NDVI_eos_value",
  "EVI_sos_value", "EVI_pos_value", "EVI_eos_value", 
  "SAVI_sos_value", "SAVI_pos_value", "SAVI_eos_value",
  # Differences pos-sos in value and doy
  "NDVI_diff_pos_sos_value", "EVI_diff_pos_sos_value", "SAVI_diff_pos_sos_value",
  "NDVI_diff_pos_sos_doy", "EVI_diff_pos_sos_doy", "SAVI_diff_pos_sos_doy",
  # Differences pos-eos in value and doy
  "NDVI_diff_pos_eos_value", "EVI_diff_pos_eos_value","SAVI_diff_pos_eos_value",
  "NDVI_diff_eos_pos_doy", "EVI_diff_eos_pos_doy", "SAVI_diff_eos_pos_doy",
  # Growing season duration
  "NDVI_gsd", "EVI_gsd", "SAVI_gsd",
  # Canopy height
  "canopy_height")
```

## RF with all points

```{r}
filtered_data0 <- data_validation %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  # Remove all rows with wrong values of indices (not between -1 and 1)
  dplyr::filter(EVI_max <= 1 & EVI_min >= -1) %>%
  dplyr::filter(NDVI_max <= 1) %>%
  dplyr::filter(NDMI_max <= 1) %>%
  dplyr::filter(NDWI_min >= -1) %>%
  # Remove rows with missing values
  dplyr::filter(if_all(all_of(vars_RF), ~ !is.na(.))) %>%
  # Keep only rows with differences > 0
  dplyr::filter(if_all(contains("diff"), ~ .x > 0)) %>%
  # Select only variables needed
  select(EUNISa_1, all_of(vars_RF))
```

Correlation of all variables to be included in RF models:

```{r}
corrplot(filtered_data0 %>% 
           select(all_of(vars_RF)) %>%
           cor(use = "pairwise.complete.obs"),
         method = "color", type = "upper", tl.col = "black", tl.srt = 45)
```

Split into training and test data sets.

```{r}
train_indices0 <- sample(1:nrow(filtered_data0), 0.7 * nrow(filtered_data0))
train_data0 <- filtered_data0[train_indices0, ]
test_data0 <- filtered_data0[-train_indices0, ]
```

Number of points per category for filtered data:

```{r}
filtered_data0 %>% count(EUNISa_1)
```

```{r}
rf_cforest0 <- party::cforest(
  formula = reformulate(vars_RF, response = "EUNISa_1"),
  data = train_data0, 
  controls = cforest_control(mtry = round(sqrt(length(all_of(vars_RF)))),  
                             # mtry = sqrt(35) # sqrt of total n variables
                             # Default mtry = 5
                             # Bagging: mtry = NULL
                             # or = number of input variables
                             ntree = 500) # Default, try increasing
  ) 
```

```{r}
predictions_rf_cforest0 <- predict(rf_cforest0, newdata = test_data0,
                                   OOB = TRUE, type = "response")
```

Confusion matrix:

```{r}
confusionMatrix(predictions_rf_cforest0, test_data0$EUNISa_1)
```

```{r}
varimp_rf_cforest0 <- party::varimp(rf_cforest0, conditional = F) 
```

```{r eval=FALSE, include=FALSE}
varimp_rf_cond_cforest0 <- party::varimp(rf_cforest0, conditional = T)
# conditional = T adjusts for correlations between predictor variables
# Takes long!
save(varimp_rf_cond_cforest0, file = "objects/varimp_rf_cond_cforest0.Rdata")
```

Variable Importance Plot

```{r}
varimp_rf_cforest0_df <- data.frame(Variable = names(varimp_rf_cforest0),
                                    Importance = varimp_rf_cforest0)
ggplot(varimp_rf_cforest0_df,
       aes(x = reorder(Variable, Importance), y = Importance)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() + theme_minimal() +
  labs(title = "Variable Importance", x = "Variables", y = "Importance")
```

ROC curves:

```{r}
# Predict probabilities for each class
probabilities <- predict(rf_cforest0, newdata = test_data0, type = "prob")

# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T")  # Adjust if needed
prob_df <- as.data.frame(prob_matrix)

# Step 2: Prepare actual class labels
actual <- factor(test_data0$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)

# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))

# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
  roc_obj <- roc(actual_bin[, class], prob_df[[class]])
  auc_val <- round(auc(roc_obj), 3)
  data.frame(
    FPR = rev(roc_obj$specificities),
    TPR = rev(roc_obj$sensitivities),
    Class = paste0(class, " (AUC = ", auc_val, ")")
  )
}) %>% bind_rows()

# Step 5: Plot ROC curves with ggplot2
roc0 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) +
  geom_abline(linetype = "dashed", color = "gray") +
  labs(
    title = "Multiclass ROC Curves with AUC",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Class (AUC)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
roc0
```

## RF with all GPS points (diff or not)

```{r}
filtered_data1 <- data_validation %>%
  # Select only GPS points
  dplyr::filter(`Location method` == "Location with GPS" |
                  `Location method` == "Location with differential GPS") %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  # Remove all rows with wrong values of indices (not between -1 and 1)
  dplyr::filter(EVI_max <= 1 & EVI_min >= -1) %>%
  dplyr::filter(NDVI_max <= 1) %>%
  dplyr::filter(NDMI_max <= 1) %>%
  dplyr::filter(NDWI_min >= -1) %>%
  # Remove rows with missing values
  dplyr::filter(if_all(all_of(vars_RF), ~ !is.na(.))) %>%
  # Keep only rows with differences > 0
  dplyr::filter(if_all(contains("diff"), ~ .x > 0)) %>%
  # Select only variables needed
  select(EUNISa_1, all_of(vars_RF))
```

Split into training and test data sets.

```{r}
train_indices1 <- sample(1:nrow(filtered_data1), 0.7 * nrow(filtered_data1))
train_data1 <- filtered_data1[train_indices1, ]
test_data1 <- filtered_data1[-train_indices1, ]
```

Number of points per category for filtered data:

```{r}
filtered_data1 %>% count(EUNISa_1)
```

```{r}
rf_cforest1 <- party::cforest(
  formula = reformulate(vars_RF, response = "EUNISa_1"),
  data = train_data1, 
  controls = cforest_control(mtry = round(sqrt(length(all_of(vars_RF)))),  
                             # mtry = sqrt(35) # sqrt of total n variables
                             # Default mtry = 5
                             # Bagging: mtry = NULL
                             # or = number of input variables
                             ntree = 500) # Default, try increasing
  ) 
```

```{r}
predictions_rf_cforest1 <- predict(rf_cforest1, newdata = test_data1,
                                   OOB = TRUE, type = "response")
```

Confusion matrix:

```{r}
confusionMatrix(predictions_rf_cforest1, test_data1$EUNISa_1)
```

```{r}
varimp_rf_cforest1 <- party::varimp(rf_cforest1, conditional = F) 
```

```{r eval=FALSE, include=FALSE}
varimp_rf_cond_cforest1 <- party::varimp(rf_cforest1, conditional = T)
# conditional = T adjusts for correlations between predictor variables
# Takes long!
save(varimp_rf_cond_cforest1, file = "objects/varimp_rf_cond_cforest1.Rdata")
```

Variable Importance Plot

```{r}
varimp_rf_cforest1_df <- data.frame(Variable = names(varimp_rf_cforest1),
                                    Importance = varimp_rf_cforest1)
ggplot(varimp_rf_cforest1_df,
       aes(x = reorder(Variable, Importance), y = Importance)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() + theme_minimal() +
  labs(title = "Variable Importance", x = "Variables", y = "Importance")
```

ROC curves:

```{r}
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")

# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T")  # Adjust if needed
prob_df <- as.data.frame(prob_matrix)

# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)

# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))

# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
  roc_obj <- roc(actual_bin[, class], prob_df[[class]])
  auc_val <- round(auc(roc_obj), 3)
  data.frame(
    FPR = rev(roc_obj$specificities),
    TPR = rev(roc_obj$sensitivities),
    Class = paste0(class, " (AUC = ", auc_val, ")")
  )
}) %>% bind_rows()

# Step 5: Plot ROC curves with ggplot2
roc1 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) +
  geom_abline(linetype = "dashed", color = "gray") +
  labs(
    title = "Multiclass ROC Curves with AUC",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Class (AUC)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
roc1
```

## RF with ...


# OLD FROM HERE

# First validation

For T, R, S, Q habitats.

Define a set of rules for a first validation of ALL ReSurvey data. We can call these "Expert-based" rules.

Number of observations in ReSurvey from the habitats of interest:

```{r}
nrow(data_validation %>%
       dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")))
```

Number of observations in ReSurvey from the habitats of interest and with all RS data:

```{r}
nrow(data_validation %>%
       dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
       dplyr::filter(CH_data == T) %>%
       dplyr::filter(RS_data ==T) %>%
       dplyr::filter(RS_phen_data == T))
```

```{r}
data_validation_terrestrial <- data_validation %>%
  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q"))
```

## Define rules

Create column for first validation based on different indicators, where "wrong" is noted when the validation rule is not met. Include EUNIS1 confusions.

```{r}
data_validation_terrestrial %>% count(EUNISa_1, EUNIS1_conf_type)
```

Define rules:

```{r}
data_validation_terrestrial <-
  data_validation_terrestrial %>%
  mutate(
    valid_1_NDWI = case_when(
      # Points that are basically water
      NDWI_max > 0.3 ~ "wrong",
      TRUE ~ NA_character_),
    valid_1_CH = case_when(
      # T points with low CH
      EUNISa_1 == "T" & canopy_height < 8 ~ "wrong",
      # S points with low CH
      EUNISa_1 =="S" & canopy_height < 5 ~ "wrong",
      # R & Q points with high CH
      EUNISa_1 %in% c("R", "Q") & canopy_height > 2 ~ "wrong",
      TRUE ~ NA_character_),
    valid_1_NDVI = case_when(
      # T points with low NDVI_max
      EUNISa_1 == "T" & NDVI_max < 0.6 ~ "wrong",
      # S-R-Q points with low NDVI_max
      EUNISa_1 %in% c("R", "S", "Q") & NDVI_max < 0.2 ~ "wrong",
      TRUE ~ NA_character_),
    # Count how many validation rules are not met
    valid_1_count = rowSums(across(c(valid_1_NDWI, valid_1_CH, valid_1_NDVI), 
                             ~ . == "wrong"), na.rm = TRUE),
    # Points where at least 1 rule not met
    valid_1 = if_else(valid_1_count > 0, "At least 1 rule broken",
                      "No rules broken so far")
    )
```

## Plots first validation

```{r}
ggplot(data_validation_terrestrial%>%
         mutate(rules_broken = case_when(
           valid_1_count == 1 & valid_1_NDWI == "wrong" ~ "NDWI",
           valid_1_count == 1 & valid_1_NDVI == "wrong" ~ "NDVI",
           valid_1_count == 1 & valid_1_CH == "wrong" ~ "CH",
           valid_1_count == 2 &
             valid_1_NDWI == "wrong" & valid_1_NDVI == "wrong"~ "NDWI + NDVI",
           valid_1_count == 2 &
             valid_1_NDWI == "wrong" & valid_1_CH == "wrong"~ "NDWI + CH",
           valid_1_count == 2 &
             valid_1_NDVI == "wrong" & valid_1_CH == "wrong"~ "NDVI + CH",
           valid_1_count == 3 ~ "NDWI + NDVI + CH",
           TRUE ~ NA_character_
         )), 
       aes(x = valid_1_count, fill = rules_broken)) +
  geom_bar() + labs(x = "Number of broken rules")
```

```{r}
data_validation_terrestrial %>%
         mutate(rules_broken = case_when(
           valid_1_count == 1 & valid_1_NDWI == "wrong" ~ "NDWI",
           valid_1_count == 1 & valid_1_NDVI == "wrong" ~ "NDVI",
           valid_1_count == 1 & valid_1_CH == "wrong" ~ "CH",
           valid_1_count == 2 &
             valid_1_NDWI == "wrong" & valid_1_NDVI == "wrong"~ "NDWI + NDVI",
           valid_1_count == 2 &
             valid_1_NDWI == "wrong" & valid_1_CH == "wrong"~ "NDWI + CH",
           valid_1_count == 2 &
             valid_1_NDVI == "wrong" & valid_1_CH == "wrong"~ "NDVI + CH",
           valid_1_count == 3 ~ "NDWI + NDVI + CH",
           TRUE ~ NA_character_
         )) %>%
  count(rules_broken, EUNIS1_conf_type)
```

Proportion of observations not validated (so far):

```{r}
nrow(data_validation_terrestrial %>% dplyr::filter(valid_1_count > 0))/
  nrow(data_validation_terrestrial)
```

But be aware that there are still MANY missing RS data.

```{r}
ggplot(data_validation_terrestrial %>%
         mutate(diff_GPS = if_else(
           `Location method` != "Location with differential GPS" |
             is.na(`Location method`), "no", "yes")), 
       aes(x = diff_GPS, fill = valid_1)) +
  geom_bar() + labs(x = "Differential GPS")
ggplot(data_validation_terrestrial %>%
         mutate(GPS = case_when(
           `Location method` == "Location with differential GPS" ~ "yes",
           `Location method` == "Location with GPS" ~ "yes",
           is.na(`Location method`) ~ "no",
           TRUE ~ "no"
         )), 
       aes(x = GPS, fill = valid_1)) +
  geom_bar() + labs(x = "GPS")
```

Points with any rule broken and confusion between EUNIS:

```{r}
nrow(data_validation_terrestrial %>%
       dplyr::filter(EUNIS1_conf == T & valid_1_count > 0))
```

Convert to shp to look at these in GIS:

```{r}
# st_write(data_validation_terrestrial %>%
#            dplyr::filter(EUNIS1_conf == T & valid_1_count > 0) %>%
#            st_as_sf(coords = c("Lon_updated", "Lat_updated"), crs = 4326),
#          "C:/GIS/MOTIVATE/shapefiles/resurv_not_val_EUNIS_conf.shp")
```

Checked and yes

How many points with differential GPS that have at least 1 rule broken?

```{r}
nrow(data_validation_terrestrial %>%
  dplyr::filter(`Location method` == "Location with differential GPS" &
           valid_1 == "At least 1 rule broken"))
```

Convert to shp to look at these in GIS:

```{r}
# st_write(data_validation_terrestrial %>%
#            dplyr::filter(`Location method` == "Location with differential GPS" &
#                     valid_1 == "At least 1 rule broken") %>%
#            st_as_sf(coords = c("Lon_updated", "Lat_updated"), crs = 4326),
#          "C:/GIS/MOTIVATE/shapefiles/resurv_not_val_diff_GPS.shp")
```

# Maps

## Points GPS

```{r}
# Load world boundaries
world <- ne_countries(scale = "medium", returnclass = "sf")

# Calculate the extent of the points
points_GPS_extent <- data_validation %>%
  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
  dplyr::filter(S2_data == T | Landsat_data == T ) %>%
  dplyr::filter(`Location method` == "Location with differential GPS" |
           `Location method` == "Location with GPS") %>%
  summarise(lon_min = min(Lon_updated, na.rm = TRUE),
            lon_max = max(Lon_updated, na.rm = TRUE),
            lat_min = min(Lat_updated, na.rm = TRUE),
            lat_max = max(Lat_updated, na.rm = TRUE))

# Add padding to the extent (adjust as needed)
padding <- 2  # Adjust padding to your preference
x_limits <- c(points_GPS_extent$lon_min - padding,
              points_GPS_extent$lon_max + padding)
y_limits <- c(points_GPS_extent$lat_min - padding,
              points_GPS_extent$lat_max + padding)

# Create the zoomed map
ggplot() +
  geom_sf(data = world, fill = "lightblue", color = "gray") +
  geom_point(data = data_validation %>%
               dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
               dplyr::filter(S2_data == T | Landsat_data == T ) %>%
               dplyr::filter(`Location method` == "Location with differential GPS" |
           `Location method` == "Location with GPS"),
             aes(x = Lon_updated, y = Lat_updated, color = EUNISa_1),
             size = 1) +
  coord_sf(xlim = x_limits, ylim = y_limits) +
  theme_minimal()
```

Number of GPS points by Country:

```{r}
data_validation %>%
  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
  dplyr::filter(S2_data == T | Landsat_data == T ) %>%
  dplyr::filter(`Location method` == "Location with differential GPS" |
           `Location method` == "Location with GPS") %>%
  count(Country)
```

## Points ReSurvey

```{r}
# Calculate the extent of the points
points_resurvey_extent <- data_validation %>%
  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
  dplyr::filter(S2_data == T | Landsat_data == T ) %>%
  summarise(lon_min = min(Lon_updated, na.rm = TRUE),
            lon_max = max(Lon_updated, na.rm = TRUE),
            lat_min = min(Lat_updated, na.rm = TRUE),
            lat_max = max(Lat_updated, na.rm = TRUE))

# Add padding to the extent (adjust as needed)
padding <- 2  # Adjust padding to your preference
x_limits <- c(points_resurvey_extent$lon_min - padding,
              points_resurvey_extent$lon_max + padding)
y_limits <- c(points_resurvey_extent$lat_min - padding,
              points_resurvey_extent$lat_max + padding)

# Create the zoomed map
ggplot() +
  geom_sf(data = world, fill = "lightblue", color = "gray") +
  geom_point(data = data_validation %>%
               dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
               dplyr::filter(S2_data == T | Landsat_data == T ),
             aes(x = Lon_updated, y = Lat_updated, color = EUNISa_1),
             size = 1) +
  coord_sf(xlim = x_limits, ylim = y_limits) +
  theme_minimal()
```

Number of ReSurvey points by Country:

```{r}
data_validation %>%
  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")) %>%
  dplyr::filter(S2_data == T | Landsat_data == T ) %>%
  count(Country)
```

# Distributions from GPS points without rules broken so far

Create tibble with differential GPS points without rules broken so far:

```{r}
all_GPS_valid <- data_validation_terrestrial %>%
  dplyr::filter((`Location method` == "Location with differential GPS" | 
            `Location method` == "Location with GPS" ) &
           valid_1 == "No rules broken so far") 
```

## NDVI, NDMI, NDWI, SAVI and EVI

```{r}
distr_plot(all_GPS_valid,
           c("NDVI_max", "NDVI_p90", "NDVI_min", "NDVI_p10"), 
           c("NDVI max", "NDVI p90", "NDVI min", "NDVI p10"))
distr_plot(all_GPS_valid,
           c("NDMI_max", "NDMI_p90", "NDMI_min", "NDMI_p10"), 
           c("NDMI max", "NDMI p90", "NDMI min", "NDMI p10"))
distr_plot(all_GPS_valid,
           c("NDWI_max", "NDWI_p90", "NDWI_min", "NDWI_p10"), 
           c("NDWI max", "NDWI p90", "NDWI min", "NDWI p10"))
distr_plot(all_GPS_valid,
           c("SAVI_max", "SAVI_p90", "SAVI_min", "SAVI_p10"), 
           c("SAVI max", "SAVI p90", "SAVI min", "SAVI p10"))
distr_plot(all_GPS_valid %>%
             dplyr::filter(EVI_max <= 1) %>%
             dplyr::filter(EVI_min >= -1 & EVI_min <= 1),
           c("EVI_max", "EVI_p90", "EVI_min", "EVI_p10"), 
           c("EVI max", "EVI p90", "EVI min", "EVI p10"))
```

## CH

```{r}
distr_plot(all_GPS_valid, "canopy_height", "Canopy height (m)")
```

## Phenology

```{r}
distr_plot(all_GPS_valid,
           c("SOS_DOY","Peak_DOY", "EOS_DOY",
             "NDVI_at_SOS", "NDVI_at_Peak", "NDVI_at_EOS",
             "diff_Peak_SOS","diff_Peak_EOS", "Season_Length"),
           c("SOS DOY", "Peak DOY", "EOS DOY",
             "NDVI at SOS", "NDVI at Peak", "NDVI at EOS",
             "Difference Peak-SOS", "Difference Peak-EOS", "Season Length"))
```

# GPS valid points above p20 of NDVI_max and NDMI_min for each habitat

# HERE! 

Chosen NDVI_min because it was important in RF models, but let's see with new data!

```{r}
percentiles_all_GPS <- all_GPS_valid %>%
  group_by(EUNISa_1) %>%
  summarize(percentile_20_NDVI_max = quantile(NDVI_max, probs = 0.20, na.rm = T),
            percentile_20_NDMI_min = quantile(NDMI_min, probs = 0.20, na.rm = T))

all_GPS_valid <- all_GPS_valid %>%
  left_join(percentiles_all_GPS, by = "EUNISa_1") %>%
  mutate(category_NDVI_max = case_when(
    NDVI_max < percentile_20_NDVI_max ~ "below_20th",
    NDVI_max >= percentile_20_NDVI_max ~ "above_20th"),
  category_NDMI_min = case_when(
    NDMI_min < percentile_20_NDMI_min ~ "below_20th",
    NDMI_min >= percentile_20_NDMI_min ~ "above_20th"))

ggplot(data = all_GPS_valid,
       aes(x = EUNISa_1_descr, y = NDVI_max)) +
  geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8,
                   fill = "lightblue") +
  geom_point(aes(color = category_NDVI_max),
             position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
  geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
  stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
  stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1, label = length(x)),
               geom = "text", aes(label = ..label..), vjust = 0.5) +
  labs(y = "NDVI max", x = "EUNIS level 1") +
  guides(fill = FALSE, color = FALSE) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
  scale_color_manual(values = c("below_20th" = "grey", "above_20th" = "lightblue")) +
  theme_bw() + coord_flip()

ggplot(data = all_GPS_valid,
       aes(x = EUNISa_1_descr, y = NDMI_min)) +
  geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8,
                   fill = "lightblue") +
  geom_point(aes(color = category_NDMI_min),
             position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
  geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
  stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
  stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1, label = length(x)),
               geom = "text", aes(label = ..label..), vjust = 0.5) +
  labs(y = "NDMI min", x = "EUNIS level 1") +
  guides(fill = FALSE, color = FALSE) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
  scale_color_manual(values = c("below_20th" = "grey", "above_20th" = "lightblue")) +
  theme_bw() + coord_flip()
```

# RF models

Using the conditional inference version of random forest (cforest in package party). Suggested if the data are highly correlated. Cforest is more stable in deriving variable importance values in the presence of highly correlated variables, thus providing better accuracy in calculating variable importance (ref below).

Hothorn, T., Hornik, K. and Zeileis, A. (2006) Unbiased Recursive Portioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15, 651-
674. http://dx.doi.org/10.1198/106186006X133933

## All GPS points

```{r}
dplyr::filtered_data0 <- all_GPS_valid %>%
  dplyr::filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
           !is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
           !is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
           !is.na(EVI_min)) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  dplyr::filter(EVI_max <= 1 & EVI_min >= -1)
```

Split into training and test data sets.

```{r}
train_indices0 <- sample(1:nrow(dplyr::filtered_data0), 0.7 * nrow(dplyr::filtered_data0))
train_data0 <- dplyr::filtered_data0[train_indices0, ]
test_data0 <- dplyr::filtered_data0[-train_indices0, ]
```

Number of points per category for dplyr::filtered data:

```{r}
dplyr::filtered_data0 %>% count(EUNISa_1)
```

```{r}
rf_cforest0 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
                                NDMI_min + NDWI_max + NDWI_min + EVI_max +
                                EVI_min + SAVI_max + SAVI_min + canopy_height, 
                              data = train_data0,
                              controls = cforest_control(
                                mtry = 3,
                                # mtry = sqrt(11)
                                # Default mtry = 5
                                # Bagging: mtry = NULL
                                # or = number of input variables
                                ntree = 500) # Default, try increasing
                              ) 
```

```{r}
predictions_rf_cforest0 <- predict(rf_cforest0, newdata = test_data0,
                                   OOB = TRUE, type = "response")
```

Confusion matrix:

```{r}
confusionMatrix(predictions_rf_cforest0, test_data0$EUNISa_1)
```

```{r}
varimp_rf_cforest0 <- party::varimp(rf_cforest0, conditional = F) 
```

```{r eval=FALSE, include=FALSE}
varimp_rf_cond_cforest0 <- party::varimp(rf_cforest0, conditional = T)
# conditional = T adjusts for correlations between predictor variables
# Takes long!
save(varimp_rf_cond_cforest0, file = "objects/varimp_rf_cond_cforest0.Rdata")
```

Variable Importance Plot

```{r}
varimp_rf_cforest0_df <- data.frame(Variable = names(varimp_rf_cforest0),
                                    Importance = varimp_rf_cforest0)
ggplot(varimp_rf_cforest0_df,
       aes(x = reorder(Variable, Importance), y = Importance)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() + theme_minimal() +
  labs(title = "Variable Importance", x = "Variables", y = "Importance")
```

ROC curves:

```{r}
# Predict probabilities for each class
probabilities <- predict(rf_cforest0, newdata = test_data0, type = "prob")

# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T")  # Adjust if needed
prob_df <- as.data.frame(prob_matrix)

# Step 2: Prepare actual class labels
actual <- factor(test_data0$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)

# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))

# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
  roc_obj <- roc(actual_bin[, class], prob_df[[class]])
  auc_val <- round(auc(roc_obj), 3)
  data.frame(
    FPR = rev(roc_obj$specificities),
    TPR = rev(roc_obj$sensitivities),
    Class = paste0(class, " (AUC = ", auc_val, ")")
  )
}) %>% bind_rows()

# Step 5: Plot ROC curves with ggplot2
roc0 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) +
  geom_abline(linetype = "dashed", color = "gray") +
  labs(
    title = "Multiclass ROC Curves with AUC",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Class (AUC)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
roc0
```

## REVISE FROM HERE: All GPS points above p20

dplyr::filter the data to get only GPS-points above p20 of NDVI_max and NDMI_min.

```{r}
all_GPS_valid <- all_GPS_valid %>%
  select(-percentile_20_NDVI_max, -percentile_20_NDMI_min)
```

```{r}
percentiles <- all_GPS_valid %>%
  group_by(EUNISa_1) %>%
  summarize(
    percentile_20_NDVI_max = quantile(NDVI_max, 0.20, na.rm = T),
    percentile_20_NDMI_min = quantile(NDMI_min, 0.20, na.rm = T),
    percentile_80_NDVI_max = quantile(NDVI_max, 0.80, na.rm = T),
    percentile_80_NDMI_min = quantile(NDMI_min, 0.80, na.rm = T)
    )

# Join the percentiles back to the original data
all_GPS_valid <- all_GPS_valid %>%
  left_join(percentiles, by = "EUNISa_1")

# dplyr::filter rows above the 20th percentile for both variables for each category of EUNISa_1
dplyr::filtered_data1 <- all_GPS_valid %>%
  dplyr::filter(
    NDVI_max >= percentile_20_NDVI_max & NDMI_min >= percentile_20_NDMI_min
    ) %>%
  dplyr::filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
           !is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
           !is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
           !is.na(EVI_min)) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  dplyr::filter(EVI_max <= 1 & EVI_min >= -1)
```

Split into training and test data sets.

```{r}
train_indices1 <- sample(1:nrow(dplyr::filtered_data1), 0.7 * nrow(dplyr::filtered_data1))
train_data1 <- dplyr::filtered_data1[train_indices1, ]
test_data1 <- dplyr::filtered_data1[-train_indices1, ]
```

Number of points per category for dplyr::filtered data:

```{r}
dplyr::filtered_data1 %>% count(EUNISa_1)
```

Investigate package ggparty (e.g. autoplot function, and more).

TO-DO: 
Choose the hyperparameter mtry based on the square root of the number of predictor variables (Hastie et al., 2009)-

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical
learning: Data mining, inference, and prediction. Springer Science &
Business Media.

Maybe TO_DO:
We variated ntree from 50 to 800 in steps of 50, leaving mtry constant at 2. Tis parameter variation showed that ntree=500 was optimal, while higher ntree led to no further model improvement (Supplementary Fig. S10). Subsequently, the hyperparameter mtry was varied from 2 to 8 with constant ntree=500. Here, mtry=3 led to the best results in almost all cases (Supplementary Fig. S11). Consequently, we chose ntree=500 and mtry=3 for our main analysis across all study sites.

```{r}
rf_cforest1 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
                                NDMI_min + NDWI_max + NDWI_min + EVI_max +
                                EVI_min + SAVI_max + SAVI_min + canopy_height, 
                              data = train_data1,
                              controls = cforest_control(
                                mtry = 3,
                                # mtry = sqrt(11)
                                # Default mtry = 5
                                # Bagging: mtry = NULL
                                # or = number of input variables
                                ntree = 500) # Default, try increasing
                              ) 
```

```{r}
predictions_rf_cforest1 <- predict(rf_cforest1, newdata = test_data1,
                                   OOB = TRUE, type = "response")
```

Confusion matrix:

```{r}
confusionMatrix(predictions_rf_cforest1, test_data1$EUNISa_1)
```

SurrogateTree --> does not work

```{r}
varimp_rf_cforest1 <- party::varimp(rf_cforest1, conditional = F) 
```

```{r eval=FALSE, include=FALSE}
varimp_rf_cond_cforest1 <- party::varimp(rf_cforest1, conditional = T)
# conditional = T adjusts for correlations between predictor variables
# Takes long!
save(varimp_rf_cond_cforest1, file = "objects/varimp_rf_cond_cforest1.Rdata")
```

Variable Importance Plot

```{r}
varimp_rf_cforest1_df <- data.frame(Variable = names(varimp_rf_cforest1),
                                    Importance = varimp_rf_cforest1)
ggplot(varimp_rf_cforest1_df,
       aes(x = reorder(Variable, Importance), y = Importance)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() + theme_minimal() +
  labs(title = "Variable Importance", x = "Variables", y = "Importance")
```

Tree Visualization

```{r}
# Create a single conditional inference tree using ctree
single_tree1 <- ctree(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max + NDMI_min +
                       NDWI_max + NDWI_min + EVI_max + EVI_min + SAVI_max +
                       SAVI_min + canopy_height,
                     data = train_data1)

# Plot the single tree using
autoplot(single_tree1)
```

ROC curves:

```{r}
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")

# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T")  # Adjust if needed
prob_df <- as.data.frame(prob_matrix)

# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)

# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))

# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
  roc_obj <- roc(actual_bin[, class], prob_df[[class]])
  auc_val <- round(auc(roc_obj), 3)
  data.frame(
    FPR = rev(roc_obj$specificities),
    TPR = rev(roc_obj$sensitivities),
    Class = paste0(class, " (AUC = ", auc_val, ")")
  )
}) %>% bind_rows()

# Step 5: Plot ROC curves with ggplot2
roc1 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) +
  geom_abline(linetype = "dashed", color = "gray") +
  labs(
    title = "Multiclass ROC Curves with AUC",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Class (AUC)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
roc1
```


## All GPS points within IQ range

dplyr::filter the data to get only GPS-points within IQ range of NDVI_max and NDMI_min.

```{r}
IQ_ranges <- all_GPS_valid %>%
  group_by(EUNISa_1) %>%
  summarize(
    Q1_NDVI_max = quantile(NDVI_max, 0.25, na.rm = T),
    Q1_NDMI_min = quantile(NDMI_min, 0.25, na.rm = T),
    Q3_NDVI_max = quantile(NDVI_max, 0.75, na.rm = T),
    Q3_NDMI_min = quantile(NDMI_min, 0.75, na.rm = T),
    IQR_NDVI_max = IQR(NDVI_max, na.rm = TRUE),
    IQR_NDMI_min = IQR(NDMI_min, na.rm = TRUE)
    )

# Join the IQ ranges back to the original data
all_GPS_valid <- all_GPS_valid %>%
  left_join(IQ_ranges, by = "EUNISa_1")

# Filter rows within the IQR range for both variables
dplyr::filtered_data2 <- all_GPS_valid %>%
  dplyr::filter(
    (NDVI_max >= Q1_NDVI_max & NDVI_max <= Q3_NDVI_max) &
    (NDMI_min >= Q1_NDMI_min & NDMI_min <= Q3_NDMI_min)
    ) %>%
  dplyr::filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
           !is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
           !is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
           !is.na(EVI_min)) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  dplyr::filter(EVI_max <= 1 & EVI_min >= -1)
```

Split into training and test data sets.

```{r}
train_indices2 <- sample(1:nrow(filtered_data2), 0.7 * nrow(filtered_data2))
train_data2 <- filtered_data2[train_indices2, ]
test_data2 <- filtered_data2[-train_indices2, ]
```

Number of points per category for filtered data:

```{r}
filtered_data2 %>% count(EUNISa_1)
```

```{r}
rf_cforest2 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
                                NDMI_min + NDWI_max + NDWI_min + EVI_max +
                                EVI_min + SAVI_max + SAVI_min + canopy_height, 
                              data = train_data2,
                              controls = cforest_control(
                                mtry = 3,
                                # mtry = sqrt(11)
                                # Default mtry = 5
                                # Bagging: mtry = NULL
                                # or = number of input variables
                                ntree = 500) # Default, try increasing
                              ) 
```

```{r}
predictions_rf_cforest2 <- predict(rf_cforest2, newdata = test_data2,
                                   OOB = TRUE, type = "response")
```

Confusion matrix:

```{r}
confusionMatrix(predictions_rf_cforest2, test_data2$EUNISa_1)
```

```{r}
varimp_rf_cforest2 <- party::varimp(rf_cforest2, conditional = F) 
```

```{r eval=FALSE, include=FALSE}
varimp_rf_cond_cforest2 <- party::varimp(rf_cforest2, conditional = T)
# conditional = T adjusts for correlations between predictor variables
# Takes long!
save(varimp_rf_cond_cforest2, file = "objects/varimp_rf_cond_cforest2.Rdata")
```

Variable Importance Plot

```{r}
varimp_rf_cforest2_df <- data.frame(Variable = names(varimp_rf_cforest2),
                                    Importance = varimp_rf_cforest2)
ggplot(varimp_rf_cforest2_df,
       aes(x = reorder(Variable, Importance), y = Importance)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() + theme_minimal() +
  labs(title = "Variable Importance", x = "Variables", y = "Importance")
```

ROC curves:

```{r}
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")

# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T")  # Adjust if needed
prob_df <- as.data.frame(prob_matrix)

# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)

# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))

# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
  roc_obj <- roc(actual_bin[, class], prob_df[[class]])
  auc_val <- round(auc(roc_obj), 3)
  data.frame(
    FPR = rev(roc_obj$specificities),
    TPR = rev(roc_obj$sensitivities),
    Class = paste0(class, " (AUC = ", auc_val, ")")
  )
}) %>% bind_rows()

# Step 5: Plot ROC curves with ggplot2
roc2 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) +
  geom_abline(linetype = "dashed", color = "gray") +
  labs(
    title = "Multiclass ROC Curves with AUC",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Class (AUC)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
roc2
```

## All GPS points within 1.5 * IQ range

Filter the data to get only GPS-points within 1.5 * IQ range of NDVI_max and NDMI_min.

```{r}
# Filter rows within the 1.5 * IQR range for both variables
filtered_data3 <- all_GPS_valid %>%
  filter(
    (NDVI_max >= (Q1_NDVI_max - 1.5 * IQR_NDVI_max) & NDVI_max <= (Q3_NDVI_max + 1.5 * IQR_NDVI_max)) &
      (NDMI_min >= (Q1_NDMI_min - 1.5 * IQR_NDMI_min) & NDMI_min <= (Q3_NDMI_min + 1.5 * IQR_NDMI_min))
    ) %>%
  filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
           !is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
           !is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
           !is.na(EVI_min)) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  filter(EVI_max <= 1 & EVI_min >= -1)
```

Split into training and test data sets.

```{r}
train_indices3 <- sample(1:nrow(filtered_data3), 0.7 * nrow(filtered_data3))
train_data3 <- filtered_data3[train_indices3, ]
test_data3 <- filtered_data3[-train_indices3, ]
```

Number of points per category for filtered data:

```{r}
filtered_data3 %>% count(EUNISa_1)
```

```{r}
rf_cforest3 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
                                NDMI_min + NDWI_max + NDWI_min + EVI_max +
                                EVI_min + SAVI_max + SAVI_min + canopy_height, 
                              data = train_data3,
                              controls = cforest_control(
                                mtry = 3,
                                # mtry = sqrt(11)
                                # Default mtry = 5
                                # Bagging: mtry = NULL
                                # or = number of input variables
                                ntree = 500) # Default, try increasing
                              ) 
```

```{r}
predictions_rf_cforest3 <- predict(rf_cforest3, newdata = test_data3,
                                   OOB = TRUE, type = "response")
```

Confusion matrix:

```{r}
confusionMatrix(predictions_rf_cforest3, test_data3$EUNISa_1)
```

```{r}
varimp_rf_cforest3 <- party::varimp(rf_cforest3, conditional = F) 
```

```{r eval=FALSE, include=FALSE}
varimp_rf_cond_cforest3 <- party::varimp(rf_cforest3, conditional = T)
# conditional = T adjusts for correlations between predictor variables
# Takes long!
save(varimp_rf_cond_cforest3, file = "objects/varimp_rf_cond_cforest3.Rdata")
```

Variable Importance Plot

```{r}
varimp_rf_cforest3_df <- data.frame(Variable = names(varimp_rf_cforest3),
                                    Importance = varimp_rf_cforest3)
ggplot(varimp_rf_cforest3_df,
       aes(x = reorder(Variable, Importance), y = Importance)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() + theme_minimal() +
  labs(title = "Variable Importance", x = "Variables", y = "Importance")
```

ROC curves:

```{r}
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")

# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T")  # Adjust if needed
prob_df <- as.data.frame(prob_matrix)

# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)

# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))

# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
  roc_obj <- roc(actual_bin[, class], prob_df[[class]])
  auc_val <- round(auc(roc_obj), 3)
  data.frame(
    FPR = rev(roc_obj$specificities),
    TPR = rev(roc_obj$sensitivities),
    Class = paste0(class, " (AUC = ", auc_val, ")")
  )
}) %>% bind_rows()

# Step 5: Plot ROC curves with ggplot2
roc3 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) +
  geom_abline(linetype = "dashed", color = "gray") +
  labs(
    title = "Multiclass ROC Curves with AUC",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Class (AUC)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
roc3
```

## All GPS points within mean +/- SD

Filter the data to get only GPS-points within mean +/- SD of NDVI_max and NDMI_min.

```{r}
mean_sd <- all_GPS_valid %>%
  group_by(EUNISa_1) %>%
  summarize(
    mean_NDVI_max = mean(all_GPS_valid$NDVI_max, na.rm = T),
    mean_NDMI_min = mean(all_GPS_valid$NDMI_min, na.rm = T),
    sd_NDVI_max = sd(all_GPS_valid$NDVI_max, na.rm = T),
    sd_NDMI_min = sd(all_GPS_valid$NDMI_min, na.rm = T)
    )

# Join the IQ ranges back to the original data
all_GPS_valid <- all_GPS_valid %>%
  left_join(mean_sd, by = "EUNISa_1")

# Filter rows within the specified range for both variables
filtered_data4 <- all_GPS_valid %>%
  filter(
    (NDVI_max >= (mean_NDVI_max - sd_NDVI_max) & NDVI_max <= (mean_NDVI_max + sd_NDVI_max)) &
      (NDMI_min >= (mean_NDMI_min - sd_NDMI_min) & NDMI_min <= (mean_NDMI_min + sd_NDMI_min))
    ) %>%
  filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
           !is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
           !is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
           !is.na(EVI_min)) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  filter(EVI_max <= 1 & EVI_min >= -1)
```

Split into training and test data sets.

```{r}
train_indices4 <- sample(1:nrow(filtered_data4), 0.7 * nrow(filtered_data4))
train_data4 <- filtered_data4[train_indices4, ]
test_data4 <- filtered_data4[-train_indices4, ]
```

Number of points per category for filtered data:

```{r}
filtered_data4 %>% count(EUNISa_1)
```

```{r}
rf_cforest4 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
                                NDMI_min + NDWI_max + NDWI_min + EVI_max +
                                EVI_min + SAVI_max + SAVI_min + canopy_height, 
                              data = train_data4,
                              controls = cforest_control(
                                mtry = 3,
                                # mtry = sqrt(11)
                                # Default mtry = 5
                                # Bagging: mtry = NULL
                                # or = number of input variables
                                ntree = 500) # Default, try increasing
                              ) 
```

```{r}
predictions_rf_cforest4 <- predict(rf_cforest4, newdata = test_data4,
                                   OOB = TRUE, type = "response")
```

Confusion matrix:

```{r}
confusionMatrix(predictions_rf_cforest4, test_data4$EUNISa_1)
```

```{r}
varimp_rf_cforest4 <- party::varimp(rf_cforest4, conditional = F) 
```

```{r eval=FALSE, include=FALSE}
varimp_rf_cond_cforest4 <- party::varimp(rf_cforest4, conditional = T)
# conditional = T adjusts for correlations between predictor variables
# Takes long!
save(varimp_rf_cond_cforest4, file = "objects/varimp_rf_cond_cforest4.Rdata")
```

Variable Importance Plot

```{r}
varimp_rf_cforest4_df <- data.frame(Variable = names(varimp_rf_cforest4),
                                    Importance = varimp_rf_cforest4)
ggplot(varimp_rf_cforest4_df,
       aes(x = reorder(Variable, Importance), y = Importance)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() + theme_minimal() +
  labs(title = "Variable Importance", x = "Variables", y = "Importance")
```

ROC curves:

```{r}
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")

# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T")  # Adjust if needed
prob_df <- as.data.frame(prob_matrix)

# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)

# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))

# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
  roc_obj <- roc(actual_bin[, class], prob_df[[class]])
  auc_val <- round(auc(roc_obj), 3)
  data.frame(
    FPR = rev(roc_obj$specificities),
    TPR = rev(roc_obj$sensitivities),
    Class = paste0(class, " (AUC = ", auc_val, ")")
  )
}) %>% bind_rows()

# Step 5: Plot ROC curves with ggplot2
roc4 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) +
  geom_abline(linetype = "dashed", color = "gray") +
  labs(
    title = "Multiclass ROC Curves with AUC",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Class (AUC)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
roc4
```

## All GPS points above p20 and below p80

Filter the data to get only GPS-points above p20 and below p80 of NDVI_max and NDMI_min.

```{r}
# Filter rows above the 20th percentile and below the 80th percentile for both variables
filtered_data5 <- all_GPS_valid %>%
  filter(
    (NDVI_max >= percentile_20_NDVI_max & NDVI_max <= percentile_80_NDVI_max) &
    (NDMI_min >= percentile_20_NDMI_min & NDMI_min <= percentile_80_NDMI_min)
    ) %>%
  filter(!is.na(NDVI_max) & !is.na(NDMI_max) & !is.na(NDWI_max) &
           !is.na(SAVI_max) & !is.na(EVI_max) & !is.na(NDVI_min) &
           !is.na(NDMI_min) & !is.na(NDWI_min) & !is.na(SAVI_min) &
           !is.na(EVI_min)) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  filter(EVI_max <= 1 & EVI_min >= -1)
```

Split into training and test data sets.

```{r}
train_indices5 <- sample(1:nrow(filtered_data5), 0.7 * nrow(filtered_data5))
train_data5 <- filtered_data5[train_indices5, ]
test_data5 <- filtered_data5[-train_indices5, ]
```

Number of points per category for filtered data:

```{r}
filtered_data5 %>% count(EUNISa_1)
```

```{r}
rf_cforest5 <- party::cforest(EUNISa_1 ~ NDVI_max + NDVI_min + NDMI_max +
                                NDMI_min + NDWI_max + NDWI_min + EVI_max +
                                EVI_min + SAVI_max + SAVI_min + canopy_height, 
                              data = train_data5,
                              controls = cforest_control(
                                mtry = 3,
                                # mtry = sqrt(11)
                                # Default mtry = 5
                                # Bagging: mtry = NULL
                                # or = number of input variables
                                ntree = 500) # Default, try increasing
                              ) 
```

```{r}
predictions_rf_cforest5 <- predict(rf_cforest5, newdata = test_data5,
                                   OOB = TRUE, type = "response")
```

Confusion matrix:

```{r}
confusionMatrix(predictions_rf_cforest5, test_data5$EUNISa_1)
```

```{r}
varimp_rf_cforest5 <- party::varimp(rf_cforest5, conditional = F) 
```

```{r eval=FALSE, include=FALSE}
varimp_rf_cond_cforest5 <- party::varimp(rf_cforest5, conditional = T)
# conditional = T adjusts for correlations between predictor variables
# Takes long!
save(varimp_rf_cond_cforest5, file = "objects/varimp_rf_cond_cforest5.Rdata")
```

Variable Importance Plot

```{r}
varimp_rf_cforest5_df <- data.frame(Variable = names(varimp_rf_cforest5),
                                    Importance = varimp_rf_cforest5)
ggplot(varimp_rf_cforest5_df,
       aes(x = reorder(Variable, Importance), y = Importance)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() + theme_minimal() +
  labs(title = "Variable Importance", x = "Variables", y = "Importance")
```

ROC curves:

```{r}
# Predict probabilities for each class
probabilities <- predict(rf_cforest1, newdata = test_data1, type = "prob")

# Step 1: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
colnames(prob_matrix) <- c("Q", "R", "S", "T")  # Adjust if needed
prob_df <- as.data.frame(prob_matrix)

# Step 2: Prepare actual class labels
actual <- factor(test_data1$EUNISa_1, levels = c("Q", "R", "S", "T"))
classes <- levels(actual)

# Step 3: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))

# Step 4: Compute ROC data for each class with AUC in label
roc_data <- lapply(classes, function(class) {
  roc_obj <- roc(actual_bin[, class], prob_df[[class]])
  auc_val <- round(auc(roc_obj), 3)
  data.frame(
    FPR = rev(roc_obj$specificities),
    TPR = rev(roc_obj$sensitivities),
    Class = paste0(class, " (AUC = ", auc_val, ")")
  )
}) %>% bind_rows()

# Step 5: Plot ROC curves with ggplot2
roc5 <- ggplot(roc_data, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) +
  geom_abline(linetype = "dashed", color = "gray") +
  labs(
    title = "Multiclass ROC Curves with AUC",
    x = "False Positive Rate",
    y = "True Positive Rate",
    color = "Class (AUC)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
roc5
```

# HERE: Compare RF 1-5

# Cordillera data

```{r}
AlpineGrasslands_indices <- read_csv(
  "C:/Data/MOTIVATE/Cordillera/AlpineGrasslands/AlpineGrassland_Sentinel_Plot_Allyear_Allmetrics.csv")
AlpineGrasslands_phen <- read_csv(
  "C:/Data/MOTIVATE/Cordillera/AlpineGrasslands/AlpineGrasslands_Phenology_SOS_EOS_Peak_NDVI_Amplitude.csv")
AlpineGrasslands_CH <- read_csv(
  "C:/Data/MOTIVATE/Cordillera/AlpineGrasslands/AlpineGrasslands_CanopyHeight_1m.csv")
VegetationTypes_indices <- read_csv(
  "C:/Data/MOTIVATE/Cordillera/VegetationTypes/VegetationTypes_Sentinel_Plot_AllYear_Allmetrics.csv")
VegetationTypes_phen <- read_csv(
  "C:/Data/MOTIVATE/Cordillera/VegetationTypes/VegetationTypes_Phenology_SOS_EOS_Peak_NDVI_Amplitude.csv")
VegetationTypes_CH <- read_csv(
  "C:/Data/MOTIVATE/Cordillera/VegetationTypes/VegetationTypes_CanopyHeight_1m.csv")
```

```{r}
AlpineGrasslands <- AlpineGrasslands_indices %>%
  select(-`system:index`, -.geo, -Localidad) %>%
  rename(Hábitat = "H�bitat") %>% 
  full_join(AlpineGrasslands_phen  %>%
              select(-`system:index`, -.geo, -Localidad) %>%
              rename(Hábitat = "H�bitat")) %>%
  full_join(AlpineGrasslands_CH  %>%
              select(-`system:index`, -.geo, -Localidad)) %>%
  select(-Date__year, - `Precisi�n`) %>%
  mutate(DATE = ymd(DATE)) %>%
  rename(ID = "Releve_num") %>%
  mutate(ID = as.character(ID)) %>%
  mutate(layer = "AlpineGrasslands")
```

```{r}
VegetationTypes <- VegetationTypes_indices %>%
  select(-`system:index`, -.geo) %>%
  full_join(VegetationTypes_phen  %>%
              select(-`system:index`, -.geo)) %>%
  full_join(VegetationTypes_CH  %>%
              select(-`system:index`, -.geo)) %>%
  rename(Hábitat = "TYPE") %>%
  mutate(layer = "VegetationTypes")
```

Merge both datasets:

```{r}
cordillera <- bind_rows(
  AlpineGrasslands %>% select(DATE, ID, starts_with("NDMI"),
                              starts_with("NDVI"), Hábitat, "EOS_DOY",
                              "Peak_DOY", "SOS_DOY", "Season_Length",
                              "canopy_height", "layer"),
  VegetationTypes %>% select(DATE, ID, starts_with("NDMI"),
                              starts_with("NDVI"), Hábitat, "EOS_DOY",
                              "Peak_DOY", "SOS_DOY", "Season_Length",
                              "canopy_height", "layer")
  ) %>%
  mutate(EUNISa_1 = case_when(
    Hábitat = str_detect(Hábitat, "Pastizal|Cervunal|grassland|meadow") ~ "R",
    Hábitat = str_detect(Hábitat, "forest") ~ "T",
    Hábitat = str_detect(Hábitat, "Scrub|scrub|Shrubland|shrubland|shrub|Heathland") ~ "S",
    Hábitat = str_detect(Hábitat, "Suelo|Scree|scree|cliff") ~ "U",
    Hábitat = is.na(Hábitat) ~ "R",
    TRUE ~ NA_character_),
    EUNISa_1_descr = case_when(
      EUNISa_1 == "R" ~ "Grasslands",
      EUNISa_1 == "T" ~ "Forests and other wooded land",
      EUNISa_1 == "S" ~ "Heathlands, scrub and tundra",
      EUNISa_1 == "U" ~ "Inland habitats with no or little soil")
    )
```

## NDVI, NDMI

```{r}
distr_plot(cordillera,
           c("NDVI_max", "NDVI_p90", "NDVI_min", "NDVI_p10"), 
           c("NDVI max", "NDVI p90", "NDVI min", "NDVI p10"))
distr_plot(cordillera,
           c("NDMI_max", "NDMI_p90", "NDMI_min", "NDMI_p10"), 
           c("NDMI max", "NDMI p90", "NDMI min", "NDMI p10"))
```

# Session info

```{r}
sessionInfo()
```

